Publications

2025

Adding a New Publication (e.g., 2025 publication in 2025):
1. Add the new publication as the first item in the “latest-publications” reusable block.
2. Update the BibTeX and Tagged citation files by downloading and editing them from the CSE Lab Google Drive.
3. Move the last item from the “latest-publications” block to the top of the “List of publications” section below.
For any questions or assistance, feel free to contact Vittorio at vbucchieri@seas.harvard.edu

• D. Waelchli, P. Weber, M. Chatzimanolakis, R. Katzschmann, and P. Koumoutsakos, “Inverse reinforcement learning for objective discovery in collective behavior of artificial swimmers“, Physical Review Fluids, vol. 10, no. 6, pp. 064901, 2025. Inverse reinforcement learning for objective discovery in collective behavior of artificial swimmers DOI Inverse reinforcement learning for objective discovery in collective behavior of artificial swimmers PDF
• M. Balcerak, et al., “Individualizing glioma radiotherapy planning by optimization of a data and physics-informed discrete loss“, Nature Communications, vol. 16, pp. 5982, 2025. Individualizing glioma radiotherapy planning by optimization of a data and physics-informed discrete loss DOI Individualizing glioma radiotherapy planning by optimization of a data and physics-informed discrete loss PDF
• L. Amoudruz, P. Karnakov, and P. Koumoutsakos, “Contactless precision steering of particles in a fluid inside a cube with rotating walls“, Journal of Fluid Mechanics, vol. 1014, no. A15, 2025. Contactless precision steering of particles in a fluid inside a cube with rotating walls DOI Iner Contactless precision steering of particles in a fluid inside a cube with rotating walls PDF

• P. Koumoutsakos, “Machine learning and partial differential equations: benchmark, simplify, and discover“, Data-Centric Engineering, vol. 6, no. e29, 2025. Machine learning and partial differential equations: benchmark, simplify, and discoverDOI Machine learning and partial differential equations: benchmark, simplify, and discoverPDF
• H. Gao, S. Kaltenbach, and P. Koumoutsakos, “Generative learning of the solution of parametric Partial Differential Equations using guided diffusion models and virtual observations“, Computer Methods in Applied Mechanics and Engineering, vol. 435, pp. 117654, 2025. Generative learning of the solution of parametric PDEs using guided diffusion models and virtual observationsDOI Generative learning of the solution of parametric PDEs using guided diffusion models and virtual observationsPDF
• P. Karnakov, L. Amoudruz, and P. Koumoutsakos, “Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss“, Physical Review Letters, vol. 134, no. 4, pp. 044001, 2025. Optimal Navigation in Microfluidics via the Optimization of a Discrete LossDOI Optimal Navigation in Microfluidics via the Optimization of a Discrete LossPDF
• M. Balcerak, et al., “ Individualizing glioma radiotherapy planning by optimization of a data and physics-informed discrete loss “, Journal of Fluid Mechanics, vol. 1014, no. A15, 2025. Individualizing glioma radiotherapy planning by optimization of a data and physics-informed discrete loss DOI  Individualizing glioma radiotherapy planning by optimization of a data and physics-informed discrete loss PDF
• L. Amoudruz, P. Karnakov, and P. Koumoutsakos, “ Contactless precision steering of particles in a fluid inside a cube with rotating walls “, Journal of Fluid Mechanics, vol. 1014, no. A15, 2025. Contactless precision steering of particles in a fluid inside a cube with rotating walls DOI Iner Contactless precision steering of particles in a fluid inside a cube with rotating walls PDF
• D. Alexeev, S. Litvinov, A. Economides, L. Amoudruz, M. Toner, and P. Koumoutsakos, “ Quantitative 3D histochemistry reveals region-specific amyloid-$\beta$ reduction by the antidiabetic drug netoglitazone “, PLoS One, vol. 20, no. 5, pp. e0309489, 2025. Quantitative 3D histochemistry reveals region-specific amyloid-$\beta$ reduction by the antidiabetic drug netoglitazone DOI Quantitative 3D histochemistry reveals region-specific amyloid-$\beta$ reduction by the antidiabetic drug netoglitazone PDF
• D. Alexeev, S. Litvinov, A. Economides, L. Amoudruz, M. Toner, and P. Koumoutsakos, “Inertial Focusing of Spherical Particles: The Effects of Rotational Motion“, Phys. Rev. Fluids, vol. 10, no. 5, pp. 054202, 2025. Inertial Focusing of Spherical Particles: The Effects of Rotational MotionDOI Inertial Focusing of Spherical Particles: The Effects of Rotational MotionPDF
• E. Menier, S. Kaltenbach, M. Yagoubi, M. Schoenauer, and P. Koumoutsakos, “Interpretable Learning of Effective Dynamics for multiscale systems“, Proceedings A, vol. 481, no. 2305, pp. 20240167, 2025. Interpretable Learning of Effective Dynamics for multiscale systemsDOI Interpretable Learning of Effective Dynamics for multiscale systemsPDF
• L. Heimbach, S. Kaltenbach, P. Karnakov, FJ. Alexander, and P. Koumoutsakos, “Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data” Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic DataDOI
• Francesca Catto, Daniel Kirschenbaum, Athena E Economides, Anna Maria Reuss, Chiara Trevisan, Davide Caredio, Ehsan Dadgar-Kiani, Delic Mirzet, Lukas Frick, Ulrike Weber-Stadlbauer, Sergey Litvinov, Petros Koumoutsakos, Jin Hyung Lee, Adriano Aguzzi “Quantitative 3D histochemistry reveals region-specific amyloid-β reduction by the antidiabetic drug netoglitazone“, PLoS One, vol. 20, no. 5, pp. e0309489, 2025. Quantitative 3D histochemistry reveals region-specific amyloid-β reduction by the antidiabetic drug netoglitazoneDOI
• Dmitry Alexeev, Sergey Litvinov, Athena Economides, Lucas Amoudruz, Mehmet Toner, Petros Koumoutsakos “Inertial focusing of spherical particles: The effects of rotational motion“, Physical Review Fluids, vol. 10, no. 5, pp. 054202, 2025. Inertial focusing of spherical particles: The effects of rotational motionDOI
• L. Amoudruz, S. Litvinov, and P. Koumoutsakos, “ Optimal navigation of magnetic artificial microswimmers in blood capillaries with deep reinforcement learning “, Physics of Fluids, vol. 37, no. 7, pp. 071703, 2025. Optimal navigation of magnetic artificial microswimmers in blood capillaries with deep reinforcement learning DOI Optimal navigation of magnetic artificial microswimmers in blood capillaries with deep reinforcement learning PDF
• P. Fischer, S. Kaltenbach, S. Litvinov, S. Succi, and P. Koumoutsakos “Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning“, arXiv preprint, arXiv:2504.14422, 2025. Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement LearningDOI
• H. Gao, S. Kaltenbach, and P. Koumoutsakos “Generative learning of the solution of parametric partial differential equations using guided diffusion models and virtual observations“, Computer Methods in Applied Mechanics and Engineering, vol. 435, pp. 117654, 2025. Generative learning of the solution of parametric partial differential equations using guided diffusion models and virtual observationsDOI
• Michal Balcerak, Tamaz Amiranashvili, Antonio Terpin, Suprosanna Shit, Lea Bogensperger, Sebastian Kaltenbach, Petros Koumoutsakos, and Bjoern Menze “Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling“, arXiv preprint, arXiv:2504.10612, 2025. Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative ModelingDOI
• H. Gao, S. Kaltenbach, and P. Koumoutsakos “Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows“, arXiv preprint, arXiv:2502.07990, 2025. Learning Effective Dynamics across Spatio-Temporal Scales of Complex FlowsDOI

2024

• H. S. Heidenreich, P. R. Vlachas, and P. Koumoutsakos, “Deconstructing Recurrence, Attention, and Gating: Investigating the transferability of Transformers and Gated Recurrent Neural Networks in forecasting of dynamical systems“. arXiv preprint 2024, arXiv:2410.02654. Deconstructing Recurrence, Attention, and Gating: Investigating the transferability of Transformers and Gated Recurrent Neural Networks in forecasting of dynamical systemsPDF
• S. Bracha et al., “Engineering Toxoplasma gondii secretion systems for intracellular delivery of multiple large therapeutic proteins to neurons“. Nature Microbiology 2024, 9 (8), 2051–2072. Engineering Toxoplasma gondii secretion systems for intracellular delivery of multiple large therapeutic proteins to neuronsPDF
• M. Balcerak et al., “Physics-Regularized Multi-Modal Image Assimilation for Brain Tumor Localization“. Advances in Neural Information Processing Systems, vol. 37, pp. 41909–41933 2024Physics-Regularized Multi-Modal Image Assimilation for Brain Tumor Localization PDF
• F. Catto et al., “Quantitative 3D histochemistry reveals region-specific amyloid-β reduction by the antidiabetic drug netoglitazone“. bioRxiv Quantitative 3D histochemistry reveals region-specific amyloid-β reduction by the antidiabetic drug netoglitazone PDF
• J.-P. von Bassewitz, S. Kaltenbach, and P. Koumoutsakos, “Closure Discovery for Coarse-Grained Partial Differential Equations using Multi-Agent Reinforcement Learning“. arXiv preprint 2024, arXiv:2402.00972. Closure Discovery for Coarse-Grained Partial Differential Equations using Multi-Agent Reinforcement LearningPDF
• A. B. Buhendwa, D. A. Bezgin, P. Karnakov, N. A. Adams, and P. Koumoutsakos, “Shape inference in three-dimensional steady state supersonic flows using ODIL and JAX-Fluids“, arXiv preprint arXiv:2408.10094, 2024. Shape inference in three-dimensional steady state supersonic flows using ODIL and JAX-FluidsPDF
• P. Koumoutsakos, “On roads less travelled between AI and computational science“, Nature Reviews Physics, vol. 1, no. 3, 2024. On roads less travelled between AI and computational scienceDOI On roads less travelled between AI and computational sciencePDF
• M. Chatzimanolakis, P. Weber, and P. Koumoutsakos, “Learning in two dimensions and controlling in three: Generalizable drag reduction strategies for flows past circular cylinders through deep reinforcement learning“, Physical Review Fluids, vol. 9, no. 4, pp. 043902, 2024. Learning in two dimensions and controlling in three: Generalizable drag reduction strategies for flows past circular cylinders through deep reinforcement learningDOI Learning in two dimensions and controlling in three: Generalizable drag reduction strategies for flows past circular cylinders through deep reinforcement learningPDF
• P. Karnakov, S. Litvinov, and P. Koumoutsakos, “Karnakov, P., Litvinov, S., & Koumoutsakos, P. (2024). Solving inverse problems in physics by optimizing a discrete loss: Fast and accurate learning without neural networks“, PNAS Nexus, vol. 3, no. 1, pp. 005, 2024. Karnakov, P., Litvinov, S., & Koumoutsakos, P. 2024. Solving inverse problems in physics by optimizing a discrete loss: Fast and accurate learning without neural networksDOI Karnakov, P., Litvinov, S., & Koumoutsakos, P. (2024). Solving inverse problems in physics by optimizing a discrete loss: Fast and accurate learning without neural networksPDF
• H. Gao, S. Kaltenbach, and P. Koumoutsakos, “Generative learning for forecasting the dynamics of high-dimensional complex systems“, Nature Communications, vol. 15, no. 1, pp. 8904, 2024. Generative learning for forecasting the dynamics of high-dimensional complex systemsPublisher's Version Generative learning for forecasting the dynamics of high-dimensional complex systemsPDF
• L. Amoudruz, A. Economides, and P. Koumoutsakos, “The Volume of Healthy Red Blood Cells is Optimal for Advective Oxygen Transport in Arterioles“, Biophysical Journal, vol. 123, no. 10, pp. 1289-1296, 2024. The Volume of Healthy Red Blood Cells is Optimal for Advective Oxygen Transport in ArteriolesDOI The Volume of Healthy Red Blood Cells is Optimal for Advective Oxygen Transport in ArteriolesPDF
• K. Vogiatzoglou, C. Papadimitriou, K. Ampountolas, M. Chatzimanolakis, P. Koumoutsakos, and V. Bontozoglou, “An interpretable wildfire spreading model for real-time predictions“, Journal of Computational Science, vol. 83, no. 102435, 2024. An interpretable wildfire spreading model for real-time predictionsDOI An interpretable wildfire spreading model for real-time predictionsPDF
• P. R. Vlachas and P. Koumoutsakos, “Learning from Predictions: Fusing Training and Autoregressive Inference for Long-Term Spatiotemporal Forecasts“, Physica D: Nonlinear Phenomena, vol. 470, no. Part A, 2024. Learning from Predictions: Fusing Training and Autoregressive Inference for Long-Term Spatiotemporal ForecastsDOI Learning from Predictions: Fusing Training and Autoregressive Inference for Long-Term Spatiotemporal ForecastsPDF

2023

• S. Kaltenbach, P. – S. Koutsourelakis, and P. Koumoutsakos, “Interpretable reduced-order modeling with time-scale separation”, arXiv. Interpretable reduced-order modeling with time-scale separationDOI
• D. Waelchli, P. Weber, and P. Koumoutsakos, “Discovering individual rewards in collective behavior through inverse multi-agent reinforcement learning”, arXiv. Discovering individual rewards in collective behavior through inverse multi-agent reinforcement learningPublisher’s Version
• I. Kicic, P. Vlachas, G. Arampatzis, M. Chatzimanolakis, L. Guibas, and P. Koumoutsakos, “Adaptive learning of effective dynamics for online modeling of complex systems”, Computer Methods in Applied Mechanics and Engineering, 415, 116204. Adaptive learning of effective dynamics for online modeling of complex systemsDOI Adaptive learning of effective dynamics for online modeling of complex systemsPDF
• E. Papadopoulou, G. W. Kim, P. Koumoutsakos, and G. Kim, “Molecular dynamics analysis of water flow through a multiply connected carbon nanotube channel”, Current Applied Physics, 45, 64-71. Molecular dynamics analysis of water flow through a multiply connected carbon nanotube channelDOI Molecular dynamics analysis of water flow through a multiply connected carbon nanotube channelPDF
• P. Karnakov, S. Litvinov, and P. Koumoutsakos, “Flow reconstruction by multiresolution optimization of a discrete loss with automatic differentiation”, The European Physical Journal E, 46, 59. Flow reconstruction by multiresolution optimization of a discrete loss with automatic differentiationDOI “Flow reconstruction by multiresolution optimization of a discrete loss with automatic differentiationPDF
• L. Amoudruz, A. Economides, G. Arampatzis, and P. Koumoutsakos, “The stress-free state of human erythrocytes: Data-driven inference of a transferable RBC model”, Biophysical Journal. The stress-free state of human erythrocytes: Data-driven inference of a transferable RBC modelDOI The stress-free state of human erythrocytes: Data-driven inference of a transferable RBC modelPDF

2022

• M. Chatzimanolakis, P. Weber, and P. Koumoutsakos, “Vortex separation cascades in simulations of the planar flow past an impulsively started cylinder up to Re = 100 000,”Journal of Fluid Mechanics, vol. 953, 2022. Vortex separation cascades in simulations of the planar flow past an impulsively started cylinder up to Re = 100 000DOI Vortex separation cascades in simulations of the planar flow past an impulsively started cylinder up to Re = 100 000PDF
• S. Cantero-Chinchilla, et al., “Robust optimal sensor configuration using the value of information,”Structural Control and Health Monitoring, 2022. Robust optimal sensor configuration using the value of informationPublisher’s Version Robust optimal sensor configuration using the value of informationPDF
• Y. Gal, P. Koumoutsakos, F. Lanusse, G. Louppe, and C. Papadimitriou, “Bayesian uncertainty quantification for machine-learned models in physics,”Nature Reviews Physics , 2022. Bayesian uncertainty quantification for machine-learned models in physicsDOI Bayesian uncertainty quantification for machine-learned models in physicsPDF
• P. R. Vlachas, G. Arampatzis, C. Uhler, and P. Koumoutsakos, “Multiscale simulations of complex systems by learning their effective dynamics,”Nat Mach Intell, 2022. Multiscale simulations of complex systems by learning their effective dynamicsDOI Multiscale simulations of complex systems by learning their effective dynamicsPDF
• J. Lipkova, B. Menze, B. Wiestler, P. Koumoutsakos, and J. Lowengrub, “Modelling glioma progression, mass effect and intracranial pressure in patient anatomy,”J. R. Soc. Interface, vol. 19, no. 188, 2022. Modelling glioma progression, mass effect and intracranial pressure in patient anatomyDOI Modelling glioma progression, mass effect and intracranial pressure in patient anatomyPDF
• J. Bae and P. KOUMOUTSAKOS, “Scientific multi-agent reinforcement learning for wall-models of turbulent flows,”Nature Communications, vol. 13, 2022. Scientific multi-agent reinforcement learning for wall-models of turbulent flowsDOI Scientific multi-agent reinforcement learning for wall-models of turbulent flowsPDF
• P. R. Vlachas, J. Zavadlav, M. Praprotnik, and P. Koumoutsakos, “Accelerated Simulations of Molecular Systems through Learning of Effective Dynamics,”J. Chem. Theory Comput. vol. 18, no. 1, pp. 538-549, 2022. Accelerated Simulations of Molecular Systems through Learning of Effective DynamicsPublisher’s Version Accelerated Simulations of Molecular Systems through Learning of Effective DynamicsPDF

2021

• K. Daniilidis, et al., “Robotics in the AI era: a vision for a hellenic robotics initiative,” Found. Trends Mach. Learn. vol. 9, no. 3, pp. 201-265, 2021. Robotics in the AI era: a vision for a hellenic robotics initiativePublisher’s Version Robotics in the AI era: a vision for a hellenic robotics initiativePDF
• L. Amoudruz and P. Koumoutsakos, “Independent Control and Path Planning of Microswimmers with a Uniform Magnetic Field,” Advanced Intelligent Systems, vol. 2100183, 2021. Independent Control and Path Planning of Microswimmers with a Uniform Magnetic FieldPublisher’s Version Independent Control and Path Planning of Microswimmers with a Uniform Magnetic FieldPDF
• Y. Yang, N. Karvounis, J. H. Walther, H. Ding, and C. Wen, “Effect of area ratio of the primary nozzle on steam ejector performance considering nonequilibrium condensations,” Energy, vol. 237, pp. 121483, 2021. Effect of area ratio of the primary nozzle on steam ejector performance considering nonequilibrium condensationsPublisher’s Version Effect of area ratio of the primary nozzle on steam ejector performance considering nonequilibrium condensationsBibTex
• P. Gunnarson, I. Mandralis, G. Novati, P. Koumoutsakos, and J. Dabiri, “Learning efficient navigation in vortical flow fields,” Nature Communications, vol. 12, no. 1, 2021. Learning efficient navigation in vortical flow fieldsPublisher’s Version Learning efficient navigation in vortical flow fieldsPDF
• S. M. Martin, D. Wälchli, G. Arampatzis, A. E. Economides, P. Karnakov, and P. Koumoutsakos, “Efficient and scalable software framework for Bayesian uncertainty quantification and stochastic optimization,” Comput. Method. Appl. M. 2021. Efficient and scalable software framework for Bayesian uncertainty quantification and stochastic optimizationPublisher’s Version Efficient and scalable software framework for Bayesian uncertainty quantification and stochastic optimizationPDF
• E. Papadopoulou, J. Zavadlav, R. Podgornik, M. Praprotnik, and P. Koumoutsakos, “Tuning the Dielectric Response of Water in Nanoconfinement through Surface Wettability,” ACS Nano, 2021. Tuning the Dielectric Response of Water in Nanoconfinement through Surface WettabilityPublisher’s Version Tuning the Dielectric Response of Water in Nanoconfinement through Surface WettabilityPDF
• E. Papadopoulou, C. M. Megaridis, J. H. Walther, and P. Koumoutsakos, “Nanopumps without Pressure Gradients: Ultrafast Transport of Water in Patterned Nanotubes,” J. Phys, Chem. B, 2021. Nanopumps without Pressure Gradients: Ultrafast Transport of Water in Patterned NanotubesPublisher’s Version Nanopumps without Pressure Gradients: Ultrafast Transport of Water in Patterned NanotubesPDF
• I. Mandralis, P. Weber, G. Novati, and P. Koumoutsakos, “Learning swimming escape patterns for larval fish under energy constraints,” Phys. Rev. Fluids, vol. 6, pp. 093101, 2021. Learning swimming escape patterns for larval fish under energy constraintsPublisher’s Version Learning swimming escape patterns for larval fish under energy constraintsPDF
• K. Larson et al., “Data-driven prediction and origin identification of epidemics in population networks,” Royal Society Open Science, 2021. Data-driven prediction and origin identification of epidemics in population networksPublisher’s Version
• J. C. Ong, K. M. Pang, X.-S. Bai, M. Jangid, and J. H. Walther, “Large-eddy simulation of n-dodecane spray flame: Effects of nozzle diameter on autoignition at varying ambient temperatures,” Proceedings of the Combustion Institute, vol. 38, no. 2, pp. 3427-3434, 2021. Large-eddy simulation of n-dodecane spray flame: Effects of nozzle diameter on autoignition at varying ambient temperaturesPublisher’s Version
• A. Nemati, M. V. Jensen, K. M. Pang, and J. H. Walther, “Conjugate heat transfer simulation of sulfuric acid condensation in a large two-stroke marine engine – the effect of thermal initial condition,” Applied Thermal Engineering, vol. 195, pp. 117075, 2021. Conjugate heat transfer simulation of sulfuric acid condensation in a large two-stroke marine engine – the effect of thermal initial conditionPublisher’s Version
• M. P. Brenner and P. Koumoutsakos, “Editorial: Machine Learning and Physical Review Fluids: An Editorial Perspective,” Physical Review Fluids, vol. 6, no. 7, pp. 070001, 2021. Editorial: Machine Learning and Physical Review Fluids: An Editorial PerspectivePublisher’s Version Editorial: Machine Learning and Physical Review Fluids: An Editorial PerspectivePDF
• J. C. Ong, K. M. Pang, M. Jangi, X. – S. Bai, and J. H. Walther, “Numerical study of the influence of turbulence-chemistry interaction model on {URANS} Simulations of diesel spray flame structures under marine engine-like conditions,” Energy Fuels, vol. 35, no. 14, pp. 11457–11467, 2021. Numerical study of the influence of turbulence-chemistry interaction model on {URANS} Simulations of diesel spray flame structures under marine engine-like conditionsPublisher’s Version
• A. Khosronejad, S. Kang, F. Wermelinger, P. Koumoutsakos, and F. Sotiropoulos, “A computational study of expiratory particle transport and vortex dynamics during breathing with and without face masks,” Physics of Fluids, vol. 33, no. 6, pp. 066605, 2021. A computational study of expiratory particle transport and vortex dynamics during breathing with and without face masksPublisher’s Version A computational study of expiratory particle transport and vortex dynamics during breathing with and without face masksPDF
• M. Rønne, J. H. L. Allan, and Walther, J.H., “The nose-up effect in twin-box bridge flutter –- experimental observations and theoretical model,” Wind and structures, vol. 32, no. 4, pp. 293–308, 2021. The nose-up effect in twin-box bridge flutter –- experimental observations and theoretical modelPublisher’s Version The nose-up effect in twin-box bridge flutter –- experimental observations and theoretical modelPDF
• B. U. Anabaraonye, J. R. Bentzon, I. Khaliqdad, K. L. Feilberg, S. I. Andersen, and J. H. Walther, “The influence of turbulent transport in reactive processes: a combined numerical and experimental investigation in a Taylor-Couette reactor,” Chemical engineering journal, vol. 421, pp. 129591, 2021. The influence of turbulent transport in reactive processes: a combined numerical and experimental investigation in a Taylor-Couette reactorPDF
• A. Economides, et al., “Hierarchical Bayesian Uncertainty Quantification for a Model of the Red Blood Cell,” American Physical Society (APS), vol. 15, no. 13, 2021. Hierarchical Bayesian Uncertainty Quantification for a Model of the Red Blood CellPublisher’s Version Hierarchical Bayesian Uncertainty Quantification for a Model of the Red Blood CellPDF Hierarchical Bayesian Uncertainty Quantification for a Model of the Red Blood CellBibTex
• J. C. Ong, K. M. Pang, J. H. Walther, J. Ho, and H. K. Ng, “Effect of ambient oxygen and density on primary soot size under diesel-like conditions using a Lagrangian Soot Tracking model,” International journal of engines, vol. 14, no. 2, pp. 03-14-02-0018, 2021. Effect of ambient oxygen and density on primary soot size under diesel-like conditions using a Lagrangian Soot Tracking modelPDF
• G. Novati, H. L. de Laroussilhe, and P. Koumoutsakos, “Automating turbulence modelling by multi-agent reinforcement learning,” Nature Machine Intelligence, vol. 3, pp. 87–96, 2021. Automating turbulence modelling by multi-agent reinforcement learningPDF
• J. C. Ong, K. M. Pang, and J. H. Walther, “Prediction method for ignition delay time of liquid spray combustion in constant volume chamber,” Fuel, vol. 287, pp. 119539, 2021. Prediction method for ignition delay time of liquid spray combustion in constant volume chamberPublisher’s Version
• S. Sørensen, S. S. Asadzadeh, and J. H. Walther, “Hydrodynamics of prey capture and transportation in choanoflagellates,” Fluids, vol. 6, no. 3, pp. 94, 2021. Hydrodynamics of prey capture and transportation in choanoflagellatesPDF
• M. Zhang, J. C. Ong, K. M. Pang, X. Bai, and J. H. Walther, “An investigation on early evolution of soot in n-dodecane spray combustion using large eddy simulation,” Fuel, vol. 293, pp. 120072, 2021. An investigation on early evolution of soot in n-dodecane spray combustion using large eddy simulationPDF
• J. Mortensen, J. F. Fauerholt, H. H. Emil, and Walther, J.H., “Discrete element modelling of track ballast capturing the true shape of ballast stones,” Powder technology, vol. 386, pp. 144–153, 2021. Discrete element modelling of track ballast capturing the true shape of ballast stonesPDF
• R. Molinaro, J. Singh, S. Catsoulis, C. Narayanan, and D. Lakehal, “Embedding data analytics and CFD into the digital twin concept,” Computers & Fluids, vol. 214, pp. 104759, 2021. Embedding data analytics and CFD into the digital twin conceptPDF
• J. C. Ong, J. H. Walther, S. Xu, S. Zhong, X. Bai, and K. M. Pang, “Effects of ambient pressure and nozzle diameter on ignition characteristics in diesel spray combustion,” Fuel, vol. 290, pp. 119887, 2021. Effects of ambient pressure and nozzle diameter on ignition characteristics in diesel spray combustionPDF

2020

• P. Karnakov, S. Litvinov, J. M. Favre, and P. Koumoutsakos, “Breaking waves: to foam or not to foam?” Phys. Rev. Fluids, vol. 5, no. 11, 2020. Breaking waves: to foam or not to foam?Publisher's Version
• P. Karnakov, F. Wermelinger, S. Litvinov, and P. Koumoutsakos, “Aphros: High Performance Software for Multiphase Flows with Large Scale Bubble and Drop Clusters,” in PASC ’20: Proceedings of the Platform for Advanced Scientific Computing Conference, PA ’20, 2020, pp. 1-10. Aphros: High Performance Software for Multiphase Flows with Large Scale Bubble and Drop ClustersDOI
• M. Chatzimanolakis, et al., “Optimal allocation of limited test resources for the quantification of COVID-19 infections,” Swiss Medical Weekly, vol. 150, no. w20445, 2020. Optimal allocation of limited test resources for the quantification of COVID-19 infectionsDOI
• P. R. Vlachas, et al., “Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics,” Neural Networks, vol. 126, pp. 191 – 217, 2020. Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamicsPDF
• X. Bian, S. Litvinov, and P. Koumoutsakos, “Bending models of lipid bilayer membranes: Spontaneous curvature and area-difference elasticity,” Comput. Method. Appl. M. vol. 359, pp. 112758, 2020. Bending models of lipid bilayer membranes: Spontaneous curvature and area-difference elasticityPDF
• Z. Y. Wan, P. Karnakov, P. Koumoutsakos, and T. P. Sapsis, “Bubbles in turbulent flows: Data-driven, kinematic models with history terms,” Int. J. Multiphas. Flow, vol. 129, pp. 103286, 2020. Bubbles in turbulent flows: Data-driven, kinematic models with history termsPDF
• P. Karnakov, et al., “Data-driven inference of the reproduction number for COVID-19 before and after interventions for 51 European countries,” Swiss Medical Weekly, no. 150:w20313, 2020. Data-driven inference of the reproduction number for COVID-19 before and after interventions for 51 European countriesPDF
• P. Karnakov, S. Litvinov, and P. Koumoutsakos, “A hybrid particle volume-of-fluid method for curvature estimation in multiphase flows,” Int. J. Multiphas. Flow, vol. 125, pp. 103209, 2020. A hybrid particle volume-of-fluid method for curvature estimation in multiphase flowsPDF
• S. L. Brunton, B. R. Noack, and P. Koumoutsakos, “Machine Learning for Fluid Mechanics,” Annu. Rev. Fluid Mech. vol. 52, no. 1, pp. 477-508, 2020. Machine Learning for Fluid MechanicsPDF
• D. Alexeev, L. Amoudruz, S. Litvinov, and P. Koumoutsakos, “Mirheo: High-performance mesoscale simulations for microfluidics,” Comput. Phys. Commun. pp. 107298, 2020. Mirheo: High-performance mesoscale simulations for microfluidicsPDF
• P. Weber, G. Arampatzis, G. Novati, S. Verma, C. Papadimitriou, and P. Koumoutsakos, “Optimal Flow Sensing for Schooling Swimmers,” Biomimetics, vol. 5, no. 1, 2020. Optimal Flow Sensing for Schooling SwimmersPDF
• S. S. Asadzadeh, T. Kiørboe, P. S. Larsen, S. P. Leys, G. Yahel, and J. H. Walther, “Hydrodynamics of sponge pumps and evolution of the sponge body plan,” eLife, vol. 9, 2020. Hydrodynamics of sponge pumps and evolution of the sponge body planPDF
• J. R. Bentzon, A. Vural, K. L. Feilberg, and J. H. Walther, “Surface Wetting In Multiphase Pipe-Flow,” Multiphase science and technology, vol. 32, no. 2, pp. 137-154, 2020. Surface Wetting In Multiphase Pipe-FlowPDF
• J. C. Ong, K. M. Pang, and J. H. Walther, “Prediction method for ignition delay time of liquid spray combustion in constant volume chamber,” Fuel, vol. 287, pp. 119539, 2020. Prediction method for ignition delay time of liquid spray combustion in constant volume chamberPDF
• J. C. Ong, K. M. Pang, X. Bai, M. Jangi, and J. H. Walther, “Large-eddy simulation of n-dodecane spray flame: effects of nozzle diameters on autoignition at varying ambient temperatures,” Proceedings of the Combustion Institute, vol. 38, no. 2, pp. 3427-3434, 2020. Large-eddy simulation of n-dodecane spray flame: effects of nozzle diameters on autoignition at varying ambient temperaturesPDF
• J. C. Ong, K. M. Pang, X. Bai, M. Jangi, and J. H. Walther, “Large-eddy simulation of n-dodecane spray flame: effects of nozzle diameter on autoignition at varying ambient temperatures,” Proceedings of the Combustion Institute, vol. 38, pp. 3427-3434, 2020. Large-eddy simulation of n-dodecane spray flame: effects of nozzle diameter on autoignition at varying ambient temperaturesPDF
• A. Nemati, J. C. Ong, M. V. Jensen, K. M. Pang, S. Mayer, and J. H. Walther, “Numerical study of the scavenging process in a large two-stroke marine engine using urans and les turbulence models,” Society of Automotive Engineers (SAE) Powertrains, Fuels and Lubricants Meeting (PF&L), pp. 9, 2020. Numerical study of the scavenging process in a large two-stroke marine engine using urans and les turbulence modelsPDF
• E. Wagemann, D. Becerra, J. H. Walther, and H. A. Zambrano, “Water flow enhancement in amorphous silica nanochannels coated with monolayer graphene,” Materials Research Society Communications, vol. 10, no. 3, pp. 1-6, 2020. Water flow enhancement in amorphous silica nanochannels coated with monolayer graphenePDF
• D. Wälchli, et al., “Load Balancing in Large Scale Bayesian Inference,” in PASC ’20: Proceedings of the Platform for Advanced Scientific Computing Conference, PASC ’20, 2020, pp. 1-12. Load Balancing in Large Scale Bayesian InferenceDOI
• K. Vontas, et al., “Droplet impact on suspended metallic meshes: effects of wettability, reynolds and weber numbers,” Fluids, vol. 5, no. 2, pp. 81, 2020. Droplet impact on suspended metallic meshes: effects of wettability, reynolds and weber numbersPDF
• H. Mikkelsen and J. H. Walther, “Effect of roughness in full-scale validation of a CFD model of self-propelled ships,” Applied Ocean Research, vol. 99, no. 1, pp. 102162, 2020. Effect of roughness in full-scale validation of a CFD model of self-propelled shipsPDF
• C. Wen, N. Karvounis, J. H. Walther, H. Ding, and Y. Yang, “Non-equilibrium condensation of water vapour in supersonic flows with shock waves,” International Journal of Heat and Mass Transfer, vol. 149, pp. 119109, 2020. Non-equilibrium condensation of water vapour in supersonic flows with shock wavesPDF
• J. Canton, “Critical point for bifurcation cascades and featureless turbulence,” Physical Review Letters, vol. 124, no. 1, 2020. Critical point for bifurcation cascades and featureless turbulencePDF
• J. Lipkova, et al., “Peak of the iceberg”, in The art of theoretical biology, F. Matthäus and S. Matthäus, Ed. Springer, 2020, pp. 18-19. Peak of the iceberg PDF Peak of the iceberg BibTex
• F. Cailliez, et al., “Bayesian calibration of force fields for molecular simulations,” in Uncertainty quantification in multiscale materials modeling, Y. Wang and D. McDowell, Ed. Elsevier, 2020, pp. 169-277. Bayesian calibration of force fields for molecular simulationsPDF
• J. Lipkova, D. Rossinelli, P. Koumoutsakos, and B. Menze, “Out of the comfort zone”, in The art of theoretical biology, F. Matthäus, S. Matthäus, S. Harris, and T. Hillen, Ed. Springer, 2020, pp. 110-111. Out of the comfort zonePDF Out of the comfort zoneBibTex
• S. S. Asadzadeh, T. Kiørboe, P. S. Larsen, S. P. Leys, G. Yahel, and J. H. Walther, “Hydrodynamics of sponge pumps and evolution of the sponge body plan,” eLife, vol. 9, 2020. Hydrodynamics of sponge pumps and evolution of the sponge body planPublisher’s Version Hydrodynamics of sponge pumps and evolution of the sponge body planPDF
• J. R. Bentzon, A. Vural, K. L. Feilberg, and J. H. Walther, “Surface Wetting In Multiphase Pipe-Flow,” Multiphase science and technology, vol. 32, no. 2, pp. 137–154, 2020. Surface Wetting In Multiphase Pipe-FlowPublisher’s Version Surface Wetting In Multiphase Pipe-FlowPDF
• J. C. Ong, K. M. Pang, and J. H. Walther, “Prediction method for ignition delay time of liquid spray combustion in constant volume chamber,” Fuel, vol. 287, pp. 119539, 2020. Prediction method for ignition delay time of liquid spray combustion in constant volume chamberPublisher’s Version Prediction method for ignition delay time of liquid spray combustion in constant volume chamberPDF
• J. C. Ong, K. M. Pang, X. Bai, M. Jangi, and J. H. Walther, “Large-eddy simulation of n-dodecane spray flame: effects of nozzle diameter on autoignition at varying ambient temperatures,” Proceedings of the Combustion Institute, vol. 38, pp. 3427–3434, 2020. Large-eddy simulation of n-dodecane spray flame: effects of nozzle diameter on autoignition at varying ambient temperaturesPublisher’s Version Large-eddy simulation of n-dodecane spray flame: effects of nozzle diameter on autoignition at varying ambient temperaturesPDF
• J. C. Ong, K. M. Pang, X. Bai, M. Jangi, and J. H. Walther, “Large-eddy simulation of n-dodecane spray flame: effects of nozzle diameters on autoignition at varying ambient temperatures,” Proceedings of the Combustion Institute, vol. 38, no. 2, pp. 3427–3434, 2020. Large-eddy simulation of n-dodecane spray flame: effects of nozzle diameters on autoignition at varying ambient temperaturesPublisher’s Version Large-eddy simulation of n-dodecane spray flame: effects of nozzle diameters on autoignition at varying ambient temperaturesPDF
• A. Nemati, J. C. Ong, M. V. Jensen, K. M. Pang, S. Mayer, and J. H. Walther, “Numerical study of the scavenging process in a large two-stroke marine engine using urans and les turbulence models,” Society of Automotive Engineers (SAE) Powertrains, Fuels and Lubricants Meeting (PF&L), pp. 9, 2020. Numerical study of the scavenging process in a large two-stroke marine engine using urans and les turbulence modelsPublisher’s Version Numerical study of the scavenging process in a large two-stroke marine engine using urans and les turbulence modelsPDF
• E. Wagemann, D. Becerra, J. H. Walther, and H. A. Zambrano, “Water flow enhancement in amorphous silica nanochannels coated with monolayer graphene,” Materials Research Society Communications, vol. 10, no. 3, pp. 1-6, 2020. Water flow enhancement in amorphous silica nanochannels coated with monolayer graphenePublisher’s Version Water flow enhancement in amorphous silica nanochannels coated with monolayer graphenePDF
• H. Mikkelsen and J. H. Walther, “Effect of roughness in full-scale validation of a CFD model of self-propelled ships,” Applied Ocean Research, vol. 99, no. 1, pp. 102162, 2020. Effect of roughness in full-scale validation of a CFD model of self-propelled shipsPublisher’s Version Effect of roughness in full-scale validation of a CFD model of self-propelled shipsPDF
• K. Vontas, et al., “Droplet impact on suspended metallic meshes: effects of wettability, reynolds and weber numbers,” Fluids, vol. 5, no. 2, pp. 81, 2020. Droplet impact on suspended metallic meshes: effects of wettability, reynolds and weber numbersPublisher’s Version Droplet impact on suspended metallic meshes: effects of wettability, reynolds and weber numbersPDF
• C. Wen, N. Karvounis, J. H. Walther, H. Ding, and Y. Yang, “Non-equilibrium condensation of water vapour in supersonic flows with shock waves,” International Journal of Heat and Mass Transfer, vol. 149, pp. 119109, 2020. Non-equilibrium condensation of water vapour in supersonic flows with shock wavesPublisher’s Version Non-equilibrium condensation of water vapour in supersonic flows with shock wavesPDF
• J. Canton, “Critical point for bifurcation cascades and featureless turbulence,” Physical Review Letters, vol. 124, no. 1, 2020. Critical point for bifurcation cascades and featureless turbulencePublisher’s Version Critical point for bifurcation cascades and featureless turbulencePDF

2019

• J. Zavadlav, S. J. Marrink, and M. Praprotnik, “SWINGER: a clustering algorithm for concurrent coupling of atomistic and supramolecular liquids,” Interface Focus, vol. 9, no. 3, pp. 20180075, 2019. SWINGER: a clustering algorithm for concurrent coupling of atomistic and supramolecular liquidsPublisher’s Version SWINGER: a clustering algorithm for concurrent coupling of atomistic and supramolecular liquidsPDF
• B. Rogie, W. B. Markussen, J. H. Walther, and M. R. Kærn, “Numerical investigation of air-side heat transfer and pressure drop characteristics of a new triangular finned microchannel evaporator with water drainage slits,” Fluids, vol. 4, no. 4, pp. 205, 2019. Numerical investigation of air-side heat transfer and pressure drop characteristics of a new triangular finned microchannel evaporator with water drainage slitsPublisher’s Version Numerical investigation of air-side heat transfer and pressure drop characteristics of a new triangular finned microchannel evaporator with water drainage slitsPDF
• H. Taghavifar, A. Nemati, and J. H. Walther, “Combustion and exergy analysis of multi-component diesel-DME-methanol blends in HCCI engine,” Energy, vol. 187, pp. 115951, 2019. Combustion and exergy analysis of multi-component diesel-DME-methanol blends in HCCI enginePublisher’s Version Combustion and exergy analysis of multi-component diesel-DME-methanol blends in HCCI enginePDF
• E. Wagemann, J. H. Walther, E. R. Cruz-Chú, and H. A. Zambrano, “Water flow in silica nanopores coated by carbon nanotubes from a wetting translucency perspective,” Journal of Physical Chemistry, vol. 123, no. 42, pp. 25635–25642, 2019. Water flow in silica nanopores coated by carbon nanotubes from a wetting translucency perspectivePublisher’s Version Water flow in silica nanopores coated by carbon nanotubes from a wetting translucency perspectivePDF
• C. S. Hemmingsen, et al., “Multiphase coupling of a reservoir simulator and computational fluid dynamics for accurate near-well flow,” Journal of Petroleum Science and Engineering, vol. 178, pp. 517–527, 2019. Multiphase coupling of a reservoir simulator and computational fluid dynamics for accurate near-well flowPublisher’s Version Multiphase coupling of a reservoir simulator and computational fluid dynamics for accurate near-well flowPDF
• M. M. Hejlesen, G. Winckelmans, and J. H. Walther, “Non-singular green’s functions for the unbounded poisson equation in one, two and three dimensions,” Applied Mathematics Letters, vol. 89, pp. 28–34, 2019. Non-singular green’s functions for the unbounded poisson equation in one, two and three dimensionsPublisher’s Version Non-singular green’s functions for the unbounded poisson equation in one, two and three dimensionsPDF
• C. Wen, N. Karvounis, J. H. Walther, Y. Yan, Y. Feng, and Y. Yang, “An efficient approach to separate CO₂ using supersonic flows for carbon capture and storage,” Applied Energy, vol. 238, pp. 311–319, 2019. An efficient approach to separate CO2 using supersonic flows for carbon capture and storagePublisher’s Version An efficient approach to separate CO2 using supersonic flows for carbon capture and storagePDF
• K. M. Pang, M. Jangi, X. Bai, J. Schramm, J. H. Walther, and P. Glarborg, “Effects of ambient pressure on ignition and flame characteristics in diesel spray combustion,” Fuel, vol. 237, pp. 676–685, 2019. Effects of ambient pressure on ignition and flame characteristics in diesel spray combustionPublisher’s Version Effects of ambient pressure on ignition and flame characteristics in diesel spray combustionPDF
• S. S. Asadzadeh, P. S. Larsen, H. U. Riisgård, and J. H. Walther, “Hydrodynamics of the leucon sponge pump,” Journal of The Royal Society Interface, vol. 16, no. 150, pp. 20180630, 2019. Hydrodynamics of the leucon sponge pumpPublisher’s Version Hydrodynamics of the leucon sponge pumpPDF
• S. S. Asadzadeh, et al., “Hydrodynamic functionality of the lorica in choanoflagellates,” Journal of The Royal Society Interface, vol. 16, no. 150, pp. 20180478, 2019. Hydrodynamic functionality of the lorica in choanoflagellatesPublisher’s Version Hydrodynamic functionality of the lorica in choanoflagellatesPDF
• P. Karnakov, S. Litvinov, J. M. Favre, and P. Koumoutsakos, “Breaking waves: to foam or not to foam?” in 72nd Annual Meeting of the APS Division of Fluid Dynamics – Gallery of Fluid Motion Award Winner, 2019. Breaking waves: to foam or not to foam?Publisher’s Version Breaking waves: to foam or not to foam?Video
• P. Karnakov, F. Wermelinger, M. Chatzimanolakis, S. Litvinov, and P. Koumoutsakos, “A High Performance Computing Framework for Multiphase, Turbulent Flows on Structured Grids”, in PASC ’19: Proceedings of the Platform for Advanced Scientific Computing Conference, PASC ’19, 2019, pp. 1–9. A High Performance Computing Framework for Multiphase, Turbulent Flows on Structured GridsPublisher’s Version A High Performance Computing Framework for Multiphase, Turbulent Flows on Structured GridsPDF
• G. Arampatzis, D. Wälchli, P. Weber, H. Rästas, and P. Koumoutsakos, “(Μ,Łambda)-CCMA-ES for Constrained Optimization with an Application in Pharmacodynamics”, in PASC ’19: Proceedings of the Platform for Advanced Scientific Computing Conference, PASC ’19, 2019, pp. 1–9. (Μ,Łambda)-CCMA-ES for Constrained Optimization with an Application in PharmacodynamicsPublisher’s Version (Μ,Łambda)-CCMA-ES for Constrained Optimization with an Application in PharmacodynamicsPDF
• G. Novati and P. Koumoutsakos, “Remember and Forget for Experience Replay”, Proceedings of the 36th International Conference on Machine Learning, vol. 97, pp. 4851–4860, 2019. Remember and Forget for Experience ReplayPublisher’s Version Remember and Forget for Experience ReplayPDF
• G. Novati, L. Mahadevan, and P. Koumoutsakos, “Controlled gliding and perching through deep-reinforcement-learning”, Phys. Rev. Fluids, vol. 4, no. 9, 2019. Controlled gliding and perching through deep-reinforcement-learningPublisher’s Version Controlled gliding and perching through deep-reinforcement-learningPDF
• J. Lipková, et al., “Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans and Bayesian Inference”, IEEE T. Med. Imaging, pp. 1-1, 2019. Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans and Bayesian InferencePublisher’s Version Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans and Bayesian InferencePDF
• K. Larson, C. Bowman, C. Papadimitriou, P. Koumoutsakos, and A. Matzavinos, “Detection of arterial wall abnormalities via Bayesian model selection”, Roy. Soc. Open Sci. vol. 6, no. 10, pp. 182229, 2019. Detection of arterial wall abnormalities via Bayesian model selectionPublisher’s Version Detection of arterial wall abnormalities via Bayesian model selectionPDF
• S. M. H. Hashemi, et al., “A versatile and membrane-less electrochemical reactor for the electrolysis of water and brine”, Energ. Environ. Sci. 2019. A versatile and membrane-less electrochemical reactor for the electrolysis of water and brinePublisher’s Version A versatile and membrane-less electrochemical reactor for the electrolysis of water and brinePDF
• C. Dietsche, B. R. Mutlu, J. F. Edd, P. Koumoutsakos, and M. Toner, “Dynamic particle ordering in oscillatory inertial microfluidics”, Microfluid. Nanofluid. vol. 23, no. 6, 2019. Dynamic particle ordering in oscillatory inertial microfluidicsPublisher’s Version Dynamic particle ordering in oscillatory inertial microfluidicsPDF
• J. Zavadlav, G. Arampatzis, and P. Koumoutsakos, “Bayesian selection for coarse-grained models of liquid water”, Sci. Rep.-UK, vol. 9, no. 1, 2019. Bayesian selection for coarse-grained models of liquid waterPublisher’s Version Bayesian selection for coarse-grained models of liquid waterPDF
• W. Byeon, et al., “Automated identification and deep classification of cut marks on bones and its paleoanthropological implications”, J. Comput. Sci. vol. 32, pp. 36 – 43, 2019. Automated identification and deep classification of cut marks on bones and its paleoanthropological implicationsPublisher’s Version Automated identification and deep classification of cut marks on bones and its paleoanthropological implicationsPDF
• S. Verma, C. Papadimitriou, N. Luethen, G. Arampatzis, and P. Koumoutsakos, “Optimal sensor placement for artificial swimmers”, J. Fluid Mech. vol. 884, 2019. Optimal sensor placement for artificial swimmersPublisher’s Version Optimal sensor placement for artificial swimmersPDF
• U. Rasthofer, F. Wermelinger, P. Karnakov, J. Šukys, and P. Koumoutsakos, “Computational study of the collapse of a cloud with 12500 gas bubbles in a liquid”, Phys. Rev. Fluids, vol. 4, pp. 063602, 2019. Computational study of the collapse of a cloud with 12500 gas bubbles in a liquidPublisher’s Version Computational study of the collapse of a cloud with 12500 gas bubbles in a liquidPDF
• E. Papadopoulou, C. M. Megaridis, J. H. Walther, and P. Koumoutsakos, “Ultrafast Propulsion of Water Nanodroplets on Patterned Graphene”, ACS Nano, 2019. Ultrafast Propulsion of Water Nanodroplets on Patterned GraphenePublisher’s Version Ultrafast Propulsion of Water Nanodroplets on Patterned GraphenePDF

2018

• K. M. Pang, M. Jangi, X. Bai, J. Schramm, and J. H. Walther, “Modelling of diesel spray flames under engine-like conditions using an accelerated eulerian stochastic field method,” Combustion and Flame, vol. 193, pp. 363–383, 2018. Modelling of diesel spray flames under engine-like conditions using an accelerated eulerian stochastic field methodPublisher’s Version Modelling of diesel spray flames under engine-like conditions using an accelerated eulerian stochastic field methodPDF
• N. K. Karna, A. R. Crisson, E. Wagemann, J. H. Walther, and H. A. Zambrano, “Effect of an external electric field on capillary filling of water in hydrophilic silica nanochannels,” Physical Chemistry Chemical Physics, vol. 20, no. 27, pp. 18262–18270, 2018. Effect of an external electric field on capillary filling of water in hydrophilic silica nanochannelsPublisher’s Version Effect of an external electric field on capillary filling of water in hydrophilic silica nanochannelsPDF
• N. Ramos-García, H. J. Spietz, J. N. Sørensen, and J. H. Walther, “Vortex simulations of wind turbines operating in atmospheric conditions using a prescribed velocity-vorticity boundary layer model,” Wind Energy, vol. 21, no. 11, pp. 1216–1231, 2018. Vortex simulations of wind turbines operating in atmospheric conditions using a prescribed velocity-vorticity boundary layer modelPublisher’s Version Vortex simulations of wind turbines operating in atmospheric conditions using a prescribed velocity-vorticity boundary layer modelPDF
• J. Zavadlav, J. Sablić, R. Podgornik, and M. Praprotnik, “Open-boundary molecular dynamics of a DNA molecule in a hybrid explicit/implicit salt solution,” Biophysical Journal, vol. 114, no. 10, pp. 2352–2362, 2018. Open-boundary molecular dynamics of a DNA molecule in a hybrid explicit/implicit salt solutionPublisher’s Version Open-boundary molecular dynamics of a DNA molecule in a hybrid explicit/implicit salt solutionPDF
• H. J. Spietz, M. M. Hejlesen, and J. H. Walther, “A regularization method for solving the Poisson equation for mixed unbounded-periodic domains,” Journal of Computational Physics, vol. 356, pp. 439–447, 2018. A regularization method for solving the Poisson equation for mixed unbounded-periodic domainsPublisher’s Version A regularization method for solving the Poisson equation for mixed unbounded-periodic domainsPDF
• J. Zavadlav, S. J. Marrink, and M. Praprotnik, “Multiscale simulation of protein hydration using the SWINGER dynamical clustering algorithm,” Journal of Chemical Theory and Computation, vol. 14, no. 3, pp. 1754–1761, 2018. Multiscale simulation of protein hydration using the SWINGER dynamical clustering algorithmPublisher’s Version Multiscale simulation of protein hydration using the SWINGER dynamical clustering algorithmPDF
• N. Karvounis, K. M. Pang, S. Mayer, and J. H. Walther, “Numerical simulation of condensation of sulfuric acid and water in a large two-stroke marine diesel engine,” Applied Energy, vol. 211, pp. 1009–1020, 2018. Numerical simulation of condensation of sulfuric acid and water in a large two-stroke marine diesel enginePublisher’s Version Numerical simulation of condensation of sulfuric acid and water in a large two-stroke marine diesel enginePDF
• R. Podgornik, J. Zavadlav, and M. Praprotnik, “Molecular dynamics simulation of high density DNA arrays,” Computation, vol. 6, no. 1, pp. 3, 2018. Molecular dynamics simulation of high density DNA arraysPublisher’s Version Molecular dynamics simulation of high density DNA arraysPDF
• E. Oyarzua, J. H. Walther, and H. A. Zambrano, “Water thermophoresis in carbon nanotubes: the interplay between thermophoretic and friction forces,” Physical Chemistry Chemical Physics, vol. 20, no. 5, pp. 3672–3677, 2018. Water thermophoresis in carbon nanotubes: the interplay between thermophoretic and friction forcesPublisher’s Version Water thermophoresis in carbon nanotubes: the interplay between thermophoretic and friction forcesPDF
• J. C. Ong, K. M. Pang, J. H. Walther, J. Ho, and H. K. Ng, “Evaluation of a lagrangian soot tracking method for the prediction of primary soot particle size under engine-like conditions,” Journal of Aerosol Science, vol. 115, pp. 70–95, 2018. Evaluation of a lagrangian soot tracking method for the prediction of primary soot particle size under engine-like conditionsPublisher’s Version Evaluation of a lagrangian soot tracking method for the prediction of primary soot particle size under engine-like conditionsPDF
• M. L. Szanyi, C. S. Hemmingsen, W. Yan, J. H. Walther, and S. L. Glimberg, “Near-wellbore modeling of a horizontal well with computational fluid dynamics,” Journal of Petroleum Science and Engineering, vol. 160, pp. 119–128, 2018. Near-wellbore modeling of a horizontal well with computational fluid dynamicsPublisher’s Version Near-wellbore modeling of a horizontal well with computational fluid dynamicsPDF
• W. Byeon, Q. Wang, R. K. Srivastava, P. Koumoutsakos, “ContextVP: Fully Context-Aware Video Prediction”, ECCV 2018 oral presentation, pp. 19, 2018. ContextVP: Fully Context-Aware Video PredictionPublisher’s Version ContextVP: Fully Context-Aware Video PredictionPDF
• Z. Y. Wan, P. R. Vlachas, P. Koumoutsakos, and T. P. Sapsis, “Data-assisted reduced-order modeling of extreme events in complex dynamical systems,” PLoS ONE, vol. 13, no. 5, pp. 1-22, 2018. Data-assisted reduced-order modeling of extreme events in complex dynamical systemsPublisher’s Version Data-assisted reduced-order modeling of extreme events in complex dynamical systemsPDF
• P. R. Vlachas, W. Byeon, Z. Y. Wan, T. P. Sapsis, and P. Koumoutsakos, “Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks,” P. Roy. Soc. A-Math. Phy. vol. 474, no. 2213, pp. 20170844, 2018. Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networksPublisher’s Version Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networksPDF
• S. Verma, G. Novati, and P. Koumoutsakos, “Efficient collective swimming by harnessing vortices through deep reinforcement learning,” P. Natl. Acad. Sci. pp. 201800923, 2018. Efficient collective swimming by harnessing vortices through deep reinforcement learningPublisher’s Version Efficient collective swimming by harnessing vortices through deep reinforcement learningPDF
• J. Šukys, U. Rasthofer, F. Wermelinger, P. Hadjidoukas, and P. Koumoutsakos, “Multilevel Control Variates for Uncertainty Quantification in Simulations of Cloud Cavitation,” SIAM J. Sci. Comput. vol. 40, no. 5, pp. B1361-B1390, 2018. Multilevel Control Variates for Uncertainty Quantification in Simulations of Cloud CavitationPublisher’s Version Multilevel Control Variates for Uncertainty Quantification in Simulations of Cloud CavitationPDF
• J. Lipková, G. Arampatzis, P. Chatelain, B. Menze, and P. Koumoutsakos, “S-Leaping: An Adaptive, Accelerated Stochastic Simulation Algorithm, Bridging tau-Leaping and R-Leaping,” B. Math. Biol. 2018. S-Leaping: An Adaptive, Accelerated Stochastic Simulation Algorithm, Bridging tau-Leaping and R-LeapingPublisher’s Version S-Leaping: An Adaptive, Accelerated Stochastic Simulation Algorithm, Bridging tau-Leaping and R-LeapingPDF
• G. Arampatzis, D. Wälchli, P. Angelikopoulos, S. Wu, P. Hadjidoukas, and P. Koumoutsakos, “Langevin Diffusion for Population Based Sampling with an Application in Bayesian Inference for Pharmacodynamics,” SIAM J. Sci. Comput. vol. 40, no. 3, pp. B788-B811, 2018. Langevin Diffusion for Population Based Sampling with an Application in Bayesian Inference for PharmacodynamicsPublisher’s Version Langevin Diffusion for Population Based Sampling with an Application in Bayesian Inference for PharmacodynamicsPDF
• S. Wu, P. Angelikopoulos, J. L. Beck, and P. Koumoutsakos, “Hierarchical Stochastic Model in Bayesian Inference for Engineering Applications: Theoretical Implications and Efficient Approximation,” ASCE-ASME J. Risk Uncertain. Eng. Sys. B, vol. 5, no. 1, pp. 011006, 2018. Hierarchical Stochastic Model in Bayesian Inference for Engineering Applications: Theoretical Implications and Efficient ApproximationPublisher’s Version Hierarchical Stochastic Model in Bayesian Inference for Engineering Applications: Theoretical Implications and Efficient ApproximationPDF
• F. Wermelinger, U. Rasthofer, P. E. Hadjidoukas, and P. Koumoutsakos, “Petascale simulations of compressible flows with interfaces,” J. Comput. Sci. vol. 26, pp. 217-225, 2018. Petascale simulations of compressible flows with interfacesPublisher’s Version Petascale simulations of compressible flows with interfacesPDF

2017

• E. Hovad, P. Larsen, J. Spangenberg, J. H. Walther, J. Thorborg, and J. H. Hattel, “Cavity prediction in sand mould production applying the DISAMATIC process“, Powder technology, vol. 321, pp. 204–217, 2017 Cavity prediction in sand mould production applying the DISAMATIC processPublisher's version Cavity prediction in sand mould production applying the DISAMATIC processPDF
• C. K. Christiansen, J. H. Walther, P. Klit, and A. Vølund, “Investigation of journal orbit and flow pattern in a dynamically loaded journal bearing“, Tribology International, vol. 114, pp. 450–457, 2017 Investigation of journal orbit and flow pattern in a dynamically loaded journal bearingPublisher's version Investigation of journal orbit and flow pattern in a dynamically loaded journal bearingPDF
• J. Zavadlav and M. Praprotnik, “Adaptive resolution simulations coupling atomistic water to dissipative particle dynamics“, Journal of Chemical Physics, vol. 147, no. 11, pp. 114110, 2017 Adaptive resolution simulations coupling atomistic water to dissipative particle dynamicsPublisher's version Adaptive resolution simulations coupling atomistic water to dissipative particle dynamicsPDF
• I. Hanasaki and J. H. Walther, “Suspended particle transport through constriction channel with Brownian motion“, Physical Review E, vol. 96, no. 2, 2017 Suspended particle transport through constriction channel with Brownian motionPublisher's version Suspended particle transport through constriction channel with Brownian motionPDF
• L. T. Nielsen, S. S. Asadzadeh, J. Dölger, J. H. Walther, T. Kiørboe, and A. Andersen, “Hydrodynamics of microbial filter feeding“, Proceedings of the National Academy of Sciences (PNAS), vol. 114, no. 35, pp. 9373–9378, 2017 Hydrodynamics of microbial filter feedingPublisher's version Hydrodynamics of microbial filter feedingPDF
• L. Fu, S. Litvinov, X. Y. Hu, and N. A. Adams, “A novel partitioning method for block-structured adaptive meshes“, Journal of Computational Physics, vol. 341, pp. 447–473, 2017 A novel partitioning method for block-structured adaptive meshesPublisher's version A novel partitioning method for block-structured adaptive meshesPDF
• N. Ramos-García, M. M. Hejlesen, J. N. Sørensen, and J. H. Walther, “Hybrid vortex simulations of wind turbines using a three-dimensional viscous-inviscid panel method“, Wind Energy, vol. 20, no. 11, pp. 1871–1889, 2017 Hybrid vortex simulations of wind turbines using a three-dimensional viscous-inviscid panel methodPublisher's version Hybrid vortex simulations of wind turbines using a three-dimensional viscous-inviscid panel methodPDF
• H. J. Spietz, M. M. Hejlesen, and J. H. Walther, “Iterative Brinkman penalization for simulation of impulsively started flow past a sphere and a circular disc“, Journal of Computational Physics, vol. 336, pp. 261–274, 2017 Iterative Brinkman penalization for simulation of impulsively started flow past a sphere and a circular discPublisher's version Iterative Brinkman penalization for simulation of impulsively started flow past a sphere and a circular discPDF
• K. M. Pang, N. Karvounis, J. H. Walther, J. Schramm, P. Glarborg, and S. Mayer, “Modelling of temporal and spatial evolution of sulphur oxides and sulphuric acid under large, two-stroke marine engine-like conditions using integrated CFD-chemical kinetics“, Applied Energy, vol. 193, pp. 60–73, 2017 Modelling of temporal and spatial evolution of sulphur oxides and sulphuric acid under large, two-stroke marine engine-like conditions using integrated CFD-chemical kineticsPublisher's version Modelling of temporal and spatial evolution of sulphur oxides and sulphuric acid under large, two-stroke marine engine-like conditions using integrated CFD-chemical kineticsPDF
• Y. Yang, J. H. Walther, Y. Yan, and C. Wen, “CFD modeling of condensation process of water vapor in supersonic flows“, Applied Thermal Engineering, vol. 115, pp. 1357–1362, 2017 CFD modeling of condensation process of water vapor in supersonic flowsPublisher's version CFD modeling of condensation process of water vapor in supersonic flowsPDF
• E. Wagemann, E. Oyarzua, J. H. Walther, and H. A. Zambrano, “Slip divergence of water flow in graphene nanochannels: the role of chirality“, Physical Chemistry Chemical Physics, vol. 19, no. 13, pp. 8646–8652, 2017 Slip divergence of water flow in graphene nanochannels: the role of chiralityPublisher's version Slip divergence of water flow in graphene nanochannels: the role of chiralityPDF
• U. Rasthofer and V. Gravemeier, “Recent developments in variational multiscale methods for large-eddy simulation of turbulent flow“, Archives of Computational Methods in Engineering, vol. 25, pp. 647–690, 2017 Recent developments in variational multiscale methods for large-eddy simulation of turbulent flowPublisher's version Recent developments in variational multiscale methods for large-eddy simulation of turbulent flowPDF
• J. J. Olsen, C. S. Hemmingsen, L. Bergmann, K. K. Nielsen, S. L. Glimberg, and J. H. Walther, “Characterization and erosion modeling of a nozzle-based inflow-control device“, Society of Petroleum Engineers Drill & Completion, vol. 32, no. 4, pp. 224-233, 2017 Characterization and erosion modeling of a nozzle-based inflow-control devicePublisher's version Characterization and erosion modeling of a nozzle-based inflow-control devicePDF
• A. Andersen, T. Bohr, T. Schnipper, and J. H. Walther, “Wake structure and thrust generation of a flapping foil in two-dimensional flow“, Journal of Fluid Mechanics, vol. 812, 2017 Wake structure and thrust generation of a flapping foil in two-dimensional flowPublisher's version Wake structure and thrust generation of a flapping foil in two-dimensional flowPDF
• D. Kim, C. Bowman, J. D. T. Bonis-O’Donnell, A. Matzavinos, and D. Stein, “Giant acceleration of DNA diffusion in an array of entropic barriers“, Physical Review Letters, vol. 118, no. 4, 2017 Giant acceleration of DNA diffusion in an array of entropic barriersPublisher's version Giant acceleration of DNA diffusion in an array of entropic barriersPDF
• G. – H. Cottet and P. Koumoutsakos, “High Order Semi-Lagrangian Particle Methods”, in Spectral and High Order Methods for Partial Differential Equations ICOSAHOM 2016, M. Bittencourt, N. Dumont, and J. S. Hesthaven, Ed. Springer, 2017, pp. 103–117. High Order Semi-Lagrangian Particle MethodsPublisher’s Version High Order Semi-Lagrangian Particle MethodsPDF
• S. Verma, G. Novati, F. Noca, and P. Koumoutsakos, “Fast Motion of Heaving Airfoils”, in Procedia Computer Science – ICCS 2017, 2017, vol. 108, pp. 235–244. Fast Motion of Heaving AirfoilsPublisher’s Version Fast Motion of Heaving AirfoilsPDF
• S. Verma, P. Hadjidoukas, P. Wirth, and P. Koumoutsakos, “Multi-objective optimization of artificial swimmers”, in 2017 IEEE Congress on Evolutionary Computation (CEC), 2017, pp. 1037–1046. Multi-objective optimization of artificial swimmersPublisher’s Version Multi-objective optimization of artificial swimmersPDF
• U. Rasthofer, F. Wermelinger, P. Hadjidoukas, and P. Koumoutsakos, “Large Scale Simulation of Cloud Cavitation Collapse”, in Procedia Computer Science – ICCS 2017, 2017, vol. 108, pp. 1763–1772. Large Scale Simulation of Cloud Cavitation CollapsePublisher’s Version Large Scale Simulation of Cloud Cavitation CollapsePDF
• P. Koumoutsakos, E. Chatzi, V. V. Krzhizhanovskaya, M. Lees, J. Dongarra, and P. M. A. Sloot, “The Art of Computational Science, Bridging Gaps – Forming Alloys. Preface for ICCS 2017”, in Procedia Computer Science – ICCS 2017, 2017, vol. 108, pp. 1–6. The Art of Computational Science, Bridging Gaps – Forming Alloys. Preface for ICCS 2017Publisher’s Version The Art of Computational Science, Bridging Gaps – Forming Alloys. Preface for ICCS 2017PDF
• A. Economides, et al., “Towards the Virtual Rheometer”, in Proceedings of the Platform for Advanced Scientific Computing – PASC ’17, 2017. Towards the Virtual RheometerPublisher’s Version Towards the Virtual RheometerPDF
• S. Wu, P. Angelikopoulos, C. Papadimitriou, and P. Koumoutsakos, “Bayesian Annealed Sequential Importance Sampling (BASIS): an unbiased version of Transitional Markov Chain Monte Carlo”, ASCE-ASME J. Risk Uncertain. Eng. Sys. B, 2017. Bayesian Annealed Sequential Importance Sampling (BASIS): an unbiased version of Transitional Markov Chain Monte CarloPublisher’s Version Bayesian Annealed Sequential Importance Sampling (BASIS): an unbiased version of Transitional Markov Chain Monte CarloPDF
• S. Verma, G. Abbati, G. Novati, and P. Koumoutsakos, “Computing the force distribution on the surface of complex, deforming geometries using vortex methods and Brinkman penalization”, Int. J. Numer. Meth. Fl. 2017. Computing the force distribution on the surface of complex, deforming geometries using vortex methods and Brinkman penalizationPublisher’s Version Computing the force distribution on the surface of complex, deforming geometries using vortex methods and Brinkman penalizationPDF
• E. Oyarzua, J. H. Walther, C. M. Megaridis, P. Koumoutsakos, and H. A. Zambrano, “Carbon Nanotubes as Thermally Induced Water Pumps”, ACS Nano, vol. 11, no. 10, pp. 9997-10002, 2017. Carbon Nanotubes as Thermally Induced Water PumpsPublisher’s Version Carbon Nanotubes as Thermally Induced Water PumpsPDF
• G. Novati, S. Verma, D. Alexeev, D. Rossinelli, W. M. Van Rees, and P. Koumoutsakos, “Synchronisation through learning for two self-propelled swimmers”, Bioinspir. Biomim. vol. 12, no. 3, pp. 036001, 2017. Synchronisation through learning for two self-propelled swimmersPublisher’s Version Synchronisation through learning for two self-propelled swimmersPDF
• B. Mosimann, et al., “Reference Ranges for Fetal Atrioventricular and Ventriculoatrial Time Intervals and Their Ratios during Normal Pregnancy”, Fetal Diagn. Ther. 2017. Reference Ranges for Fetal Atrioventricular and Ventriculoatrial Time Intervals and Their Ratios during Normal PregnancyPublisher’s Version Reference Ranges for Fetal Atrioventricular and Ventriculoatrial Time Intervals and Their Ratios during Normal PregnancyPDF
• L. Kulakova, G. Arampatzis, P. Angelikopoulos, P. Hadjidoukas, C. Papadimitriou, and P. Koumoutsakos, “Data driven inference for the repulsive exponent of the Lennard-Jones potential in molecular dynamics simulations”, Sci. Rep.-UK, vol. 7, no. 1, pp. 16576, 2017. Data driven inference for the repulsive exponent of the Lennard-Jones potential in molecular dynamics simulationsPublisher’s Version Data driven inference for the repulsive exponent of the Lennard-Jones potential in molecular dynamics simulationsPDF
• N. Karathanasopoulos, P. Angelikopoulos, C. Papadimitriou, and P. Koumoutsakos, “Bayesian identification of the tendon fascicle’s structural composition using finite element models for helical geometries”, Comput. Method. Appl. M. vol. 313, pp. 744-758, 2017. Bayesian identification of the tendon fascicle's structural composition using finite element models for helical geometriesPublisher’s Version Bayesian identification of the tendon fascicle's structural composition using finite element models for helical geometriesPDF
• E. R. Cruz-Chú, et al., “On phonons and water flow enhancement in carbon nanotubes”, Nat. Nanotechnol. vol. 12, no. 12, pp. 1106-1108, 2017. On phonons and water flow enhancement in carbon nanotubesPublisher’s Version On phonons and water flow enhancement in carbon nanotubesPDF

2016

• E. Hovad, J. Spangenberg, P. Larsen, J. H. Walther, J. Thorborg, and J. H. Hattel, “Simulating the DISAMATIC process using the discrete element method — a dynamical study of granular flow“, Powder Technology, vol. 303, pp. 228–240, 2016 Simulating the DISAMATIC process using the discrete element method — a dynamical study of granular flowPublisher's version Simulating the DISAMATIC process using the discrete element method — a dynamical study of granular flowPDF
• C. S. Hemmingsen, K. M. Ingvorsen, S. Mayer, and J. H. Walther, “LES and URANS simulations of the swirling flow in a dynamic model of a uniflow-scavenged cylinder“, International Journal of Heat and Fluid Flow, vol. 62, pp. 213–223, 2016 LES and URANS simulations of the swirling flow in a dynamic model of a uniflow-scavenged cylinderPublisher's version LES and URANS simulations of the swirling flow in a dynamic model of a uniflow-scavenged cylinderPDF
• C. Wen, Y. Yang, J. H. Walther, K. M. Pang, and Y. Feng, “Effect of delta wing on the particle flow in a novel gas supersonic separator“, Powder Technology, vol. 304, pp. 261–267, 2016 Effect of delta wing on the particle flow in a novel gas supersonic separatorPublisher's version Effect of delta wing on the particle flow in a novel gas supersonic separatorPDF
• M. M. Hejlesen and J. H. Walther, “A multiresolution method for solving the poisson equation using high order regularization“, Journal of Computational Physics, vol. 326, pp. 188–196, 2016 A multiresolution method for solving the poisson equation using high order regularizationPublisher's version A multiresolution method for solving the poisson equation using high order regularizationPDF
• N. K. Karna, E. Oyarzua, J. H. Walther, and H. A. Zambrano, “Effect of the meniscus contact angle during early regimes of spontaneous imbibition in nanochannels“, Physical Chemistry Chemical Physics, vol. 18, no. 47, pp. 31997–32001, 2016 Effect of the meniscus contact angle during early regimes of spontaneous imbibition in nanochannelsPublisher's version Effect of the meniscus contact angle during early regimes of spontaneous imbibition in nanochannelsPDF
• C. Wen, A. Li, J. H. Walther, and Y. Yang, “Effect of swirling device on flow behavior in a supersonic separator for natural gas dehydration“, Separation and Purification Technology, vol. 168, pp. 68–73, 2016 Effect of swirling device on flow behavior in a supersonic separator for natural gas dehydrationPublisher's version Effect of swirling device on flow behavior in a supersonic separator for natural gas dehydrationPDF
• M. Zayernouri and A. Matzavinos, “Fractional adams–bashforth/moulton methods: an application to the fractional keller–segel chemotaxis system“, Journal of Computational Physics, vol. 317, pp. 1–14, 2016 Fractional adams–bashforth/moulton methods: an application to the fractional keller–segel chemotaxis systemPublisher's version Fractional adams–bashforth/moulton methods: an application to the fractional keller–segel chemotaxis systemPDF
• K. M. Pang, N. Karvounis, J. H. Walther, and J. Schramm, “Numerical investigation of soot formation and oxidation processes under large two-stroke marine diesel engine-like conditions using integrated CFD-chemical kinetics“, Applied Energy, vol. 169, pp. 874–887, 2016 Numerical investigation of soot formation and oxidation processes under large two-stroke marine diesel engine-like conditions using integrated CFD-chemical kineticsPublisher's version Numerical investigation of soot formation and oxidation processes under large two-stroke marine diesel engine-like conditions using integrated CFD-chemical kineticsPDF
• S. Mishra, C. Schwab, and J. Šukys, “Multi-level Monte Carlo finite volume methods for uncertainty quantification of acoustic wave propagation in random heterogeneous layered medium“, Journal of Computational Physics, vol. 312, pp. 192–217, 2016 Multi-level Monte Carlo finite volume methods for uncertainty quantification of acoustic wave propagation in random heterogeneous layered mediumPublisher's version Multi-level Monte Carlo finite volume methods for uncertainty quantification of acoustic wave propagation in random heterogeneous layered mediumPDF
• B. O. Andersen, N. F. Nielsen, and J. H. Walther, “Numerical and experimental study of pulse-jet cleaning in fabric filters“, Powder Technology, vol. 291, pp. 284–298, 2016 Numerical and experimental study of pulse-jet cleaning in fabric filtersPublisher's version Numerical and experimental study of pulse-jet cleaning in fabric filtersPDF
• S. Litvinov, Q. Xie, X. Hu, N. Adams, and M. Ellero, “Simulation of individual polymer chains and polymer solutions with smoothed dissipative particle dynamics“, Fluids, vol. 1, no. 1, pp. 7, 2016 Simulation of individual polymer chains and polymer solutions with smoothed dissipative particle dynamicsPublisher's version Simulation of individual polymer chains and polymer solutions with smoothed dissipative particle dynamicsPDF
• D. Azarnykh, S. Litvinov, X. Bian, and N. A. Adams, “Determination of macroscopic transport coefficients of a dissipative particle dynamics solvent“, Physical Review E, vol. 93, no. 1, 2016 Determination of macroscopic transport coefficients of a dissipative particle dynamics solventPublisher's version Determination of macroscopic transport coefficients of a dissipative particle dynamics solventPDF
• N. Karathanasopoulos and P. Angelikopoulos, “Optimal structural arrangements of multilayer helical assemblies“, International Journal of Solids and Structures, vol. 78-79, pp. 1–8, 2016 Optimal structural arrangements of multilayer helical assembliesPublisher's version Optimal structural arrangements of multilayer helical assembliesPDF
• L. Kulakova, P. Angelikopoulos, P. E. Hadjidoukas, C. Papadimitriou, and P. Koumoutsakos, “Approximate Bayesian Computation for Granular and Molecular Dynamics Simulations”, in Proceedings of the Platform for Advanced Scientific Computing – PASC ’16, 2016. Approximate Bayesian Computation for Granular and Molecular Dynamics SimulationsPublisher’s Version Approximate Bayesian Computation for Granular and Molecular Dynamics SimulationsPDF
• P. E. Hadjidoukas, et al., “High throughput simulations of two-phase flows on Blue Gene/Q”, in Parallel Computing: On the Road to Exascale – ParCo 2015, 2016, vol. 27, pp. 767–776. High throughput simulations of two-phase flows on Blue Gene/QPublisher’s Version High throughput simulations of two-phase flows on Blue Gene/QPDF
• F. Wermelinger, B. Hejazialhosseini, P. Hadjidoukas, D. Rossinelli, and P. Koumoutsakos, “An Efficient Compressible Multicomponent Flow Solver for Heterogeneous CPU/GPU Architectures”, in Proceedings of the Platform for Advanced Scientific Computing – PASC ’16, 2016. An Efficient Compressible Multicomponent Flow Solver for Heterogeneous CPU/GPU ArchitecturesPublisher’s Version An Efficient Compressible Multicomponent Flow Solver for Heterogeneous CPU/GPU ArchitecturesPDF
• S. Wu, P. Angelikopoulos, G. Tauriello, C. Papadimitriou, and P. Koumoutsakos, “Fusing heterogeneous data for the calibration of molecular dynamics force fields using hierarchical Bayesian models”, J. Chem. Phys. vol. 145, no. 24, pp. 244112, 2016. Fusing heterogeneous data for the calibration of molecular dynamics force fields using hierarchical Bayesian modelsPublisher’s Version Fusing heterogeneous data for the calibration of molecular dynamics force fields using hierarchical Bayesian modelsPDF
• M. Gazzola, A. A. Tchieu, D. Alexeev, A. de Brauer, and P. Koumoutsakos, “Learning to school in the presence of hydrodynamic interactions”, J. Fluid Mech. vol. 789, pp. 726-749, 2016. Learning to school in the presence of hydrodynamic interactionsPublisher’s Version Learning to school in the presence of hydrodynamic interactionsPDF
• J. Chen, J. H. Walther, and P. Koumoutsakos, “Ultrafast cooling by covalently bonded graphene-carbon nanotube hybrid immersed in water”, Nanotechnology, vol. 27, no. 46, pp. 465705, 2016. Ultrafast cooling by covalently bonded graphene-carbon nanotube hybrid immersed in waterPublisher’s Version Ultrafast cooling by covalently bonded graphene-carbon nanotube hybrid immersed in waterPDF

2015

• C. Voglis, P. E. Hadjidoukas, K. E. Parsopoulos, D. G. Papageorgiou, I. E. Lagaris, and M. N. Vrahatis, “P-MEMPSODE: parallel and irregular memetic global optimization“, Computer Physics Communications, vol. 197, pp. 190–211, 2015 P-MEMPSODE: parallel and irregular memetic global optimizationPublisher's version P-MEMPSODE: parallel and irregular memetic global optimizationPDF
• M. M. Hejlesen, J. T. Rasmussen, P. Chatelain, and J. H. Walther, “High order poisson solver for unbounded flows“, Procedia IUTAM, vol. 18, pp. 56–65, 2015 High order poisson solver for unbounded flowsPublisher's version High order poisson solver for unbounded flowsPDF
• M. M. Hejlesen, J. T. Rasmussen, A. Larsen, and J. H. Walther, “On estimating the aerodynamic admittance of bridge sections by a mesh-free vortex method“, Journal of Wind Engineering and Industrial Aerodynamics, vol. 146, pp. 117–127, 2015 On estimating the aerodynamic admittance of bridge sections by a mesh-free vortex methodPublisher's version On estimating the aerodynamic admittance of bridge sections by a mesh-free vortex methodPDF
• E. Oyarzua, J. H. Walther, A. Mejía, and H. A. Zambrano, “Early regimes of water capillary flow in slit silica nanochannels“, Physical Chemistry Chemical Physics, vol. 17, no. 22, pp. 14731–14739, 2015 Early regimes of water capillary flow in slit silica nanochannelsPublisher's version Early regimes of water capillary flow in slit silica nanochannelsPDF
• D. Rossinelli, et al., “The In-Silico Lab-on-a-Chip: Petascale and High-Throughput Simulations of Microfluidics at Cell Resolution”, in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis – SC ’15, 2015, no. Article 2. The In-Silico Lab-on-a-Chip: Petascale and High-Throughput Simulations of Microfluidics at Cell ResolutionPublisher’s Version The In-Silico Lab-on-a-Chip: Petascale and High-Throughput Simulations of Microfluidics at Cell ResolutionPDF
• P. E. Hadjidoukas, P. Angelikopoulos, L. Kulakova, C. Papadimitriou, and P. Koumoutsakos, “Exploiting Task-Based Parallelism in Bayesian Uncertainty Quantification”, in Euro-Par 2015: Parallel Processing, Springer, 2015, pp. 532–544. Exploiting Task-Based Parallelism in Bayesian Uncertainty QuantificationPublisher’s Version Exploiting Task-Based Parallelism in Bayesian Uncertainty QuantificationPDF
• P. E. Hadjidoukas, D. Rossinelli, B. Hejazialhosseini, and P. Koumoutsakos, “From 11 to 14.4 PFLOPs: Performance Optimization for Finite Volume Flow Solver”, in Proceedings of the 3rd International Conference on Exascale Applications and Software – EASC ’15, 2015, pp. 7–12. From 11 to 14.4 PFLOPs: Performance Optimization for Finite Volume Flow SolverPublisher’s Version From 11 to 14.4 PFLOPs: Performance Optimization for Finite Volume Flow SolverPDF
• F. Huhn, W. M. van Rees, M. Gazzola, D. Rossinelli, G. Haller, and P. Koumoutsakos, “Quantitative flow analysis of swimming dynamics with coherent Lagrangian vortices”, Chaos, vol. 25, no. 8, pp. 087405, 2015. Quantitative flow analysis of swimming dynamics with coherent Lagrangian vorticesPublisher’s Version Quantitative flow analysis of swimming dynamics with coherent Lagrangian vorticesPDF
• G. Tauriello, H. M. Meyer, R. S. Smith, P. Koumoutsakos, and A. H. K. Roeder, “Variability and constancy in cellular growth of Arabidopsis sepals”, Plant Physiol. pp. 2342-2358, 2015. Variability and constancy in cellular growth of Arabidopsis sepalsPublisher’s Version Variability and constancy in cellular growth of Arabidopsis sepalsPDF
• M. M. Hejlesen, P. Koumoutsakos, A. Leonard, and J. H. Walther, “Iterative Brinkman penalization for remeshed vortex methods”, J. Comput. Phys. vol. 280, pp. 547-562, 2015. Iterative Brinkman penalization for remeshed vortex methodsPublisher’s Version Iterative Brinkman penalization for remeshed vortex methodsPDF
• G. Tauriello and P. Koumoutsakos, “A comparative study of penalization and phase field methods for the solution of the diffusion equation in complex geometries”, J. Comput. Phys. vol. 283, pp. 388-407, 2015. A comparative study of penalization and phase field methods for the solution of the diffusion equation in complex geometriesPublisher’s Version A comparative study of penalization and phase field methods for the solution of the diffusion equation in complex geometriesPDF
• P. E. Hadjidoukas, P. Angelikopoulos, C. Papadimitriou, and P. Koumoutsakos, “Pi4U: A high performance computing framework for Bayesian uncertainty quantification of complex models”, J. Comput. Phys. vol. 284, pp. 1-21, 2015. Pi4U: A high performance computing framework for Bayesian uncertainty quantification of complex modelsPublisher’s Version Pi4U: A high performance computing framework for Bayesian uncertainty quantification of complex modelsPDF
• D. Rossinelli, B. Hejazialhosseini, W. van Rees, M. Gazzola, M. Bergdorf, and P. Koumoutsakos, “MRAG-I2D: Multi-resolution adapted grids for remeshed vortex methods on multicore architectures”, J. Comput. Phys. vol. 288, pp. 1-18, 2015. MRAG-I2D: Multi-resolution adapted grids for remeshed vortex methods on multicore architecturesPublisher’s Version MRAG-I2D: Multi-resolution adapted grids for remeshed vortex methods on multicore architecturesPDF
• S. D. Finley, P. Angelikopoulos, P. KOUMOUTSAKOS, and A. S. Popel, “Pharmacokinetics of Anti-VEGF Agent Aflibercept in Cancer Predicted by Data-Driven, Molecular-Detailed Model”, CPT: PSP, vol. 4, no. 11, pp. 641-649, 2015. Pharmacokinetics of Anti-VEGF Agent Aflibercept in Cancer Predicted by Data-Driven, Molecular-Detailed ModelPublisher’s Version Pharmacokinetics of Anti-VEGF Agent Aflibercept in Cancer Predicted by Data-Driven, Molecular-Detailed ModelPDF
• P. B. de Reuille, et al., “MorphoGraphX: A platform for quantifying morphogenesis in 4D”, eLife, vol. 4, 2015. MorphoGraphX: A platform for quantifying morphogenesis in 4DPublisher’s Version MorphoGraphX: A platform for quantifying morphogenesis in 4DPDF
• J. Chen, J. H. Walther, and P. Koumoutsakos, “Covalently Bonded Graphene-Carbon Nanotube Hybrid for High-Performance Thermal Interfaces”, Adv. Funct. Mater. vol. 25, no. 48, pp. 7539-7545, 2015. Covalently Bonded Graphene-Carbon Nanotube Hybrid for High-Performance Thermal InterfacesPublisher’s Version Covalently Bonded Graphene-Carbon Nanotube Hybrid for High-Performance Thermal InterfacesPDF
• W. M. van Rees, M. Gazzola, and P. Koumoutsakos, “Optimal morphokinematics for undulatory swimmers at intermediate Reynolds numbers”, J. Fluid Mech. vol. 775, pp. 178-188, 2015. Optimal morphokinematics for undulatory swimmers at intermediate Reynolds numbersPublisher’s Version Optimal morphokinematics for undulatory swimmers at intermediate Reynolds numbersPDF
• M. U. Baumann, et al., “Placental plasticity in monochorionic twins: Impact on birth weight and placental weight”, Placenta, vol. 36, no. 9, pp. 1018-1023, 2015. Placental plasticity in monochorionic twins: Impact on birth weight and placental weightPublisher’s Version Placental plasticity in monochorionic twins: Impact on birth weight and placental weightPDF
• W. M. van Rees, G. Novati, and P. Koumoutsakos, “Self-propulsion of a counter-rotating cylinder pair in a viscous fluid”, Phys. Fluids, vol. 27, no. 6, pp. 063102, 2015. Self-propulsion of a counter-rotating cylinder pair in a viscous fluidPublisher’s Version Self-propulsion of a counter-rotating cylinder pair in a viscous fluidPDF
• P. Angelikopoulos, C. Papadimitriou, and P. Koumoutsakos, “X-TMCMC: Adaptive kriging for Bayesian inverse modeling”, Comput. Method. Appl. M. vol. 289, pp. 409-428, 2015. X-TMCMC: Adaptive kriging for Bayesian inverse modelingPublisher’s Version X-TMCMC: Adaptive kriging for Bayesian inverse modelingPDF
• A. Popadić, M. Praprotnik, P. Koumoutsakos, and J. H. Walther, “Continuum simulations of water flow past fullerene molecules”, Eur. Phys. J.-Spec. Top. vol. 224, no. 12, pp. 2321-2330, 2015. Continuum simulations of water flow past fullerene moleculesPublisher’s Version Continuum simulations of water flow past fullerene moleculesPDF
• D. Alexeev, J. Chen, J. H. Walther, K. P. Giapis, P. Angelikopoulos, and P. Koumoutsakos, “Kapitza Resistance between Few-Layer Graphene and Water: Liquid Layering Effects”, Nano Lett. vol. 15, no. 9, pp. 5744-5749, 2015. Kapitza Resistance between Few-Layer Graphene and Water: Liquid Layering EffectsPublisher’s Version Kapitza Resistance between Few-Layer Graphene and Water: Liquid Layering EffectsPDF
• P. R. Jones, et al., “Sustaining dry surfaces under water”, Sci. Rep.-UK, vol. 5, no. 1, 2015. Sustaining dry surfaces under waterPublisher’s Version Sustaining dry surfaces under waterPDF
• S. Wu, P. Angelikopoulos, C. Papadimitriou, R. Moser, and P. Koumoutsakos, “A hierarchical Bayesian framework for force field selection in molecular dynamics simulations”, Philos. T. Roy. Soc. A, vol. 374, no. 2060, pp. 20150032, 2015. A hierarchical Bayesian framework for force field selection in molecular dynamics simulationsPublisher’s Version A hierarchical Bayesian framework for force field selection in molecular dynamics simulationsPDF

2014

• M. Fasil, D. Plesner, J. H. Walther, N. Mijatovic, J. Holbøll, and B. B. Jensen, “Numerical and experimental investigation of heat flow in permanent magnet brushless DC hub motor,” SAE International Journal of Alternative Powertrains, vol. 4, no. 1, pp. 46-57, 2014. Numerical and experimental investigation of heat flow in permanent magnet brushless DC hub motorPublisher’s Version Numerical and experimental investigation of heat flow in permanent magnet brushless DC hub motorPDF
• H. A. Zambrano, J. H. Walther, and R. L. and Jaffe, “Molecular dynamics simulations of water on a hydrophilic silica surface at high air pressures,” Journal of Molecular Liquids, vol. 198, pp. 107–113, 2014. Molecular dynamics simulations of water on a hydrophilic silica surface at high air pressuresPublisher’s Version Molecular dynamics simulations of water on a hydrophilic silica surface at high air pressuresPDF
• P. E. Hadjidoukas, P. Angelikopoulos, C. Voglis, D. G. Papageorgiou, and I. E. Lagaris, “NDL-v2.0: a new version of the numerical differentiation library for parallel architectures,” Computer Physics Communications, vol. 185, no. 7, pp. 2217–2219, 2014. NDL-v2.0: a new version of the numerical differentiation library for parallel architecturesPublisher’s Version NDL-v2.0: a new version of the numerical differentiation library for parallel architecturesPDF
• E. Sigurdsson, K. M. Ingvorsen, M. V. Jensen, S. Mayer, S. Matlok, and J. H. Walther, “Numerical analysis of the scavenge flow and convective heat transfer in large two-stroke marine diesel engines,” Applied Energy, vol. 123, pp. 37–46, 2014. Numerical analysis of the scavenge flow and convective heat transfer in large two-stroke marine diesel enginesPublisher’s Version Numerical analysis of the scavenge flow and convective heat transfer in large two-stroke marine diesel enginesPDF
• K. M. Ingvorsen, K. E. Meyer, J. H. Walther, and S. Mayer, “Turbulent swirling flow in a dynamic model of a uniflow-scavenged two-stroke engine,” Experiments in Fluids, vol. 55, no. 6, pp. 1748, 2014. Turbulent swirling flow in a dynamic model of a uniflow-scavenged two-stroke enginePublisher’s Version Turbulent swirling flow in a dynamic model of a uniflow-scavenged two-stroke enginePDF
• P. E. Hadjidoukas, C. Voglis, V. V. Dimakopoulos, I. E. Lagaris, and D. G. Papageorgiou, “Supporting adaptive and irregular parallelism for non-linear numerical optimization,” Applied Mathematics and Computation, vol. 231, pp. 544–559, 2014. Supporting adaptive and irregular parallelism for non-linear numerical optimizationPublisher’s Version Supporting adaptive and irregular parallelism for non-linear numerical optimizationPDF
• A. Obeidat, et al., “Large eddy simulations of the influence of piston position on the swirling flow in a model two-stroke diesel engine,” International Journal of Numerical Methods for Heat & Fluid Flow, vol. 24, no. 2, pp. 325–341, 2014. Large eddy simulations of the influence of piston position on the swirling flow in a model two-stroke diesel enginePublisher’s Version Large eddy simulations of the influence of piston position on the swirling flow in a model two-stroke diesel enginePDF
• C. L. Felter, J. H. Walther, and C. Henriksen, “Moving least squares simulation of free surface flows,” Computers & Fluids, vol. 91, pp. 47–56, 2014. Moving least squares simulation of free surface flowsPublisher’s Version Moving least squares simulation of free surface flowsPDF
• W. M. Van Rees, D. Rossinelli, P. Hadjidoukas, and P. Koumoutsakos, “High performance CPU/GPU multiresolution Poisson solver”, Adv. Par. Com. vol. 1, no. 1, pp. 481-490, 2014. High performance CPU/GPU multiresolution Poisson solverPublisher’s Version High performance CPU/GPU multiresolution Poisson solverPDF
• A. Popadić, J. H. Walther, P. KOUMOUTSAKOS, and M. Praprotnik, “Continuum simulations of water flow in carbon nanotube membranes”, New J. Phys. vol. 16, no. 8, pp. 082001, 2014. Continuum simulations of water flow in carbon nanotube membranesPublisher’s Version Continuum simulations of water flow in carbon nanotube membranesPDF
• F. Milde, G. Tauriello, H. Haberkern, and P. Koumoutsakos, “SEM++: A particle model of cellular growth, signaling and migration”, Computational particle mechanics, vol. 1, no. 2, pp. 211-227, 2014. SEM++: A particle model of cellular growth, signaling and migrationPublisher’s Version SEM++: A particle model of cellular growth, signaling and migrationPDF
• P. E. Hadjidoukas, P. Angelikopoulos, D. Rossinelli, D. Alexeev, C. Papadimitriou, and P. Koumoutsakos, “Bayesian uncertainty quantification and propagation for discrete element simulations of granular materials”, Comput. Method. Appl. M. vol. 282, pp. 218-238, 2014. Bayesian uncertainty quantification and propagation for discrete element simulations of granular materialsPublisher’s Version Bayesian uncertainty quantification and propagation for discrete element simulations of granular materialsPDF
• M. Gazzola, B. Hejazialhosseini, and P. Koumoutsakos, “Reinforcement Learning and Wavelet Adapted Vortex Methods for Simulations of Self-propelled Swimmers”, SIAM J. Sci. Comput. vol. 36, no. 3, pp. B622-B639, 2014. Reinforcement Learning and Wavelet Adapted Vortex Methods for Simulations of Self-propelled SwimmersPublisher’s Version Reinforcement Learning and Wavelet Adapted Vortex Methods for Simulations of Self-propelled SwimmersPDF
• E. R. Cruz-Chu, A. Malafeev, T. Pajarskas, I. V. Pivkin, and P. Koumoutsakos, “Structure and Response to Flow of the Glycocalyx Layer”, Biophys. J. vol. 106, no. 1, pp. 232-243, 2014. Structure and Response to Flow of the Glycocalyx LayerPublisher’s Version Structure and Response to Flow of the Glycocalyx LayerPDF
• J. Chen, J. H. Walther, and P. Koumoutsakos, “Strain Engineering of Kapitza Resistance in Few-Layer Graphene”, Nano Lett. vol. 14, no. 2, pp. 819-825, 2014. Strain Engineering of Kapitza Resistance in Few-Layer GraphenePublisher’s Version Strain Engineering of Kapitza Resistance in Few-Layer GraphenePDF

2013

• C. Voglis, P. E. Hadjidoukas, D. G. Papageorgiou, and I. E. Lagaris, “A parallel hybrid optimization algorithm for fitting interatomic potentials,” Applied Soft Computing, vol. 13, no. 12, pp. 4481–4492, 2013. A parallel hybrid optimization algorithm for fitting interatomic potentialsPublisher’s Version A parallel hybrid optimization algorithm for fitting interatomic potentialsPDF
• M. M. Hejlesen, J. T. Rasmussen, P. Chatelain, and J. H. Walther, “A high order solver for the unbounded poisson equation,” Journal of Computational Physics, vol. 252, pp. 458–467, 2013. A high order solver for the unbounded poisson equationPublisher’s Version A high order solver for the unbounded poisson equationPDF
• M. V. Jensen and J. H. Walther, “Numerical analysis of jet impingement heat transfer at high jet Reynolds number and large temperature difference,” Heat Transfer Engineering, vol. 34, no. 10, pp. 801–809, 2013. Numerical analysis of jet impingement heat transfer at high jet Reynolds number and large temperature differencePublisher’s Version Numerical analysis of jet impingement heat transfer at high jet Reynolds number and large temperature differencePDF
• C. Papadimitriou, P. Angelikopoulos, P. KOUMOUTSAKOS, and D. C. Papadioti, “Efficient techniques for Bayesian inverse modeling of large-order computational models”, in Safety, Reliability, Risk and Life-cycle Performance of Structures and Infrastructures – ICOSSAR 2013, 2013, pp. 1937–1944. Efficient techniques for Bayesian inverse modeling of large-order computational modelsPDF
• D. Rossinelli, et al., “11 PFLOP/s simulations of cloud cavitation collapse”, in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis on – SC ’13, 2013. 11 PFLOP/s simulations of cloud cavitation collapsePublisher’s Version 11 PFLOP/s simulations of cloud cavitation collapseConference paper 11 PFLOP/s simulations of cloud cavitation collapsePDF
• W. M. van Rees, M. Gazzola, and P. Koumoutsakos, “Optimal shapes for anguilliform swimmers at intermediate Reynolds numbers”, J. Fluid Mech. vol. 722, 2013. Optimal shapes for anguilliform swimmers at intermediate Reynolds numbersPublisher’s Version Optimal shapes for anguilliform swimmers at intermediate Reynolds numbersPDF
• F. Milde, S. Lauw, P. Koumoutsakos, and M. L. Iruela-Arispe, “The mouse retina in 3D: quantification of vascular growth and remodeling”, Integrative Biology, vol. 5, no. 12, pp. 1426, 2013. The mouse retina in 3D: quantification of vascular growth and remodelingPublisher’s Version The mouse retina in 3D: quantification of vascular growth and remodelingPDF
• P. Angelikopoulos, C. Papadimitriou, and P. Koumoutsakos, “Data driven, predictive molecular dynamics for nanoscale flow simulations under uncertainty”, The Journal of Physical Chemistry B, vol. 117, no. 47, pp. 14808-14816, 2013. Data driven, predictive molecular dynamics for nanoscale flow simulations under uncertaintyPublisher’s Version Data driven, predictive molecular dynamics for nanoscale flow simulations under uncertaintyPDF
• B. Hejazialhosseini, D. Rossinelli, and P. Koumoutsakos, “3d shock-bubble interaction”, Physics of Fluids, vol. 25, no. 11, pp. 110816, 2013. 3d shock-bubble interactionPublisher’s Version 3d shock-bubble interactionPDF
• P. Koumoutsakos and J. Feigelman, “Multiscale stochastic simulations of chemical reactions with regulated scale separation”, Journal of Computational Physics, vol. 244, pp. 290-297, 2013. Multiscale stochastic simulations of chemical reactions with regulated scale separationPublisher’s Version Multiscale stochastic simulations of chemical reactions with regulated scale separationPDF
• J. H. Walther, K. Ritos, E. R. Cruz-Chu, C. M. Megaridis, and P. Koumoutsakos, “Barriers to superfast water transport in carbon nanotube membranes”, Nano Letters, vol. 13, no. 5, pp. 1910-1914, 2013. Barriers to superfast water transport in carbon nanotube membranesPublisher’s Version Barriers to superfast water transport in carbon nanotube membranesPDF
• D. Franco, et al., “Accelerated endothelial wound healing on microstructured substrates under flow”, Biomaterials, vol. 34, no. 5, pp. 1488-1497, 2013. Accelerated endothelial wound healing on microstructured substrates under flowPublisher’s Version Accelerated endothelial wound healing on microstructured substrates under flowPDF
• B. Hejazialhosseini, D. Rossinelli, and P. Koumoutsakos, “Vortex dynamics in 3d shock-bubble interaction”, Publisher, vol. 25, no. 11, pp. 110816, 2013. Vortex dynamics in 3d shock-bubble interactionPublisher’s Version Vortex dynamics in 3d shock-bubble interactionPDF
• P. Koumoutsakos, I. Pivkin, and F. Milde, “The fluid mechanics of cancer and its therapy”, Annual Review of Fluid Mechanics, vol. 45, no. 1, pp. 325-355, 2013. The fluid mechanics of cancer and its therapyPublisher’s Version The fluid mechanics of cancer and its therapyPDF
• G. Tauriello and P. Koumoutsakos, “Coupling remeshed particle and phase field methods for the simulation of reaction-diffusion on the surface and the interior of deforming geometries”, SIAM Journal on Scientific Computing, vol. 35, no. 6, pp. B1285-B1303, 2013. Coupling remeshed particle and phase field methods for the simulation of reaction-diffusion on the surface and the interior of deforming geometriesPublisher’s Version Coupling remeshed particle and phase field methods for the simulation of reaction-diffusion on the surface and the interior of deforming geometriesPDF

2012

• S. Haider, et al., “PIV study of the effect of piston position on the in-cylinder swirling flow during the scavenging process in large two-stroke marine diesel engines,” Journal of Marine Science and Technology, vol. 18, no. 1, pp. 133–143, 2012. PIV study of the effect of piston position on the in-cylinder swirling flow during the scavenging process in large two-stroke marine diesel enginesPublisher’s Version PIV study of the effect of piston position on the in-cylinder swirling flow during the scavenging process in large two-stroke marine diesel enginesPDF
• B. Hejazialhosseini, D. Rossinelli, C. Conti, P. Koumoutsakos, “High throughput software for direct numerical simulations of compressible two-phase flows”, SC ’12: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis. High throughput software for direct numerical simulations of compressible two-phase flowsPublisher’s Version High throughput software for direct numerical simulations of compressible two-phase flowsPDF
• J. H. Walther, M. Praprotnik, E. M. Kotsalis, and P. Koumoutsakos, “Multiscale simulation of water flow past a C540 fullerene”, J. Comput. Phys. vol. 231, no. 7, pp. 2677-2681, 2012. Multiscale simulation of water flow past a C540 fullerenePublisher’s Version Multiscale simulation of water flow past a C540 fullerenePDF
• W. M. van Rees, F. Hussain, and P. Koumoutsakos, “Vortex tube reconnection at Re = 10^4”, Phys. Fluids, vol. 24, no. 7, pp. 075105, 2012. Vortex tube reconnection at Re = 10^4Publisher’s Version Vortex tube reconnection at Re = 10^4PDF
• M. Paolucci, et al., “Towards a living earth simulator”, Eur. Phys. J.-Spec. Top. vol. 214, no. 1, pp. 77-108, 2012. Towards a living earth simulatorPublisher’s Version Towards a living earth simulatorPDF
• F. Milde, D. Franco, A. Ferrari, V. Kurtcuoglu, D. Poulikakos, and P. Koumoutsakos, “Cell Image Velocimetry (CIV): boosting the automated quantification of cell migration in wound healing assays”, Integr. Biol. vol. 4, no. 11, pp. 1437, 2012. Cell Image Velocimetry (CIV): boosting the automated quantification of cell migration in wound healing assaysPublisher’s Version Cell Image Velocimetry (CIV): boosting the automated quantification of cell migration in wound healing assaysPDF
• M. Gazzola, C. Mimeau, A. A. Tchieu, and P. Koumoutsakos, “Flow mediated interactions between two cylinders at finite Re numbers”, Phys. Fluids, vol. 24, no. 4, pp. 043103, 2012. Flow mediated interactions between two cylinders at finite Re numbersPublisher’s Version Flow mediated interactions between two cylinders at finite Re numbersPDF
• M. Gazzola, W. M. van Rees, and P. Koumoutsakos, “C-start: optimal start of larval fish”, J. Fluid Mech. vol. 698, pp. 5-18, 2012. C-start: optimal start of larval fishPublisher’s Version C-start: optimal start of larval fishPDF
• C. Conti, D. Rossinelli, and P. Koumoutsakos, “GPU and APU computations of Finite Time Lyapunov Exponent fields”, J. Comput. Phys. vol. 231, no. 5, pp. 2229-2244, 2012. GPU and APU computations of Finite Time Lyapunov Exponent fieldsPublisher’s Version GPU and APU computations of Finite Time Lyapunov Exponent fieldsPDF
• P. Angelikopoulos, C. Papadimitriou, and P. Koumoutsakos, “Bayesian uncertainty quantification and propagation in molecular dynamics simulations: A high performance computing framework”, J. Chem. Phys. vol. 137, no. 14, pp. 144103, 2012. Bayesian uncertainty quantification and propagation in molecular dynamics simulations: A high performance computing frameworkPublisher’s Version Bayesian uncertainty quantification and propagation in molecular dynamics simulations: A high performance computing frameworkPDF

2011

• M. Bergdorf, F. Milde, and P. Koumoutsakos, “Particle simulations of growth: application to tumorigenesis“, in Modeling tumor vasculature. Springer, 2011, pp. 261–303. Particle simulations of growth: application to tumorigenesis PDF
• F. Milde, M. Bergdorf, and P. Koumoutsakos, “Particle simulations of growth: application to angiogenesis“, in Modeling tumor vasculature. Springer, 2011, pp. 305–334. Particle simulations of growth: application to angiogenesis PDF
• H. Riisgård, et al., “The exhalant jet of mussels mytilus edulis,” Marine Ecology Progress Series, vol. 437, pp. 147–164, 2011. The exhalant jet of mussels mytilus edulisPublisher’s Version The exhalant jet of mussels mytilus edulisPDF
• J. T. Rasmussen, G. Cottet, and J. H. Walther, “A multiresolution remeshed vortex-in-cell algorithm using patches,” Journal of Computational Physics, vol. 230, no. 17, pp. 6742–6755, 2011. A multiresolution remeshed vortex-in-cell algorithm using patchesPublisher’s Version A multiresolution remeshed vortex-in-cell algorithm using patchesPDF
• P. Chatelain, M. Gazzola, S. Kern, and P. Koumoutsakos, “Optimization of Aircraft Wake Alleviation Schemes through an Evolution Strategy”, in High Performance Computing for Computational Science – VECPAR 2010, Springer, 2011, pp. 210–221. Optimization of Aircraft Wake Alleviation Schemes through an Evolution StrategyPublisher’s Version Optimization of Aircraft Wake Alleviation Schemes through an Evolution StrategyConference paper Optimization of Aircraft Wake Alleviation Schemes through an Evolution StrategyPDF
• G. Schwank, G. Tauriello, R. Yagi, E. Kranz, P. Koumoutsakos, and K. Basler, “Antagonistic Growth Regulation by Dpp and Fat Drives Uniform Cell Proliferation”, Dev. Cell, vol. 20, no. 1, pp. 123-130, 2011. Antagonistic Growth Regulation by Dpp and Fat Drives Uniform Cell ProliferationPublisher’s Version Antagonistic Growth Regulation by Dpp and Fat Drives Uniform Cell ProliferationPDF
• D. Rossinelli, B. Hejazialhosseini, D. G. Spampinato, and P. Koumoutsakos, “Multicore/Multi-GPU Accelerated Simulations of Multiphase Compressible Flows Using Wavelet Adapted Grids”, SIAM J. Sci. Comput. vol. 33, no. 2, pp. 512-540, 2011. Multicore/Multi-GPU Accelerated Simulations of Multiphase Compressible Flows Using Wavelet Adapted GridsPublisher’s Version Multicore/Multi-GPU Accelerated Simulations of Multiphase Compressible Flows Using Wavelet Adapted GridsPDF
• D. Rossinelli, C. Conti, and P. Koumoutsakos, “Mesh-particle interpolations on graphics processing units and multicore central processing units”, Philos. T. Roy. Soc. A, vol. 369, no. 1944, pp. 2164-2175, 2011. Mesh-particle interpolations on graphics processing units and multicore central processing unitsPublisher’s Version Mesh-particle interpolations on graphics processing units and multicore central processing unitsPDF
• W. M. van Rees, A. Leonard, D. I. Pullin, and P. Koumoutsakos, “A comparison of vortex and pseudo-spectral methods for the simulation of periodic vortical flows at high Reynolds numbers”, J. Comput. Phys. vol. 230, no. 8, pp. 2794-2805, 2011. A comparison of vortex and pseudo-spectral methods for the simulation of periodic vortical flows at high Reynolds numbersPublisher’s Version A comparison of vortex and pseudo-spectral methods for the simulation of periodic vortical flows at high Reynolds numbersPDF
• P. D. Koumoutsakos, B. Bayati, F. Milde, and G. Tauriello, “Particle Simulations of Morphogenesis”, Math. Mod. Meth. Appl. S. vol. 21, no. supp01, pp. 955-1006, 2011. Particle Simulations of MorphogenesisPublisher’s Version Particle Simulations of MorphogenesisPDF
• M. Gazzola, P. Chatelain, W. M. van Rees, and P. Koumoutsakos, “Simulations of single and multiple swimmers with non-divergence free deforming geometries”, J. Comput. Phys. vol. 230, no. 19, pp. 7093-7114, 2011. Simulations of single and multiple swimmers with non-divergence free deforming geometriesPublisher’s Version Simulations of single and multiple swimmers with non-divergence free deforming geometriesPDF
• M. Gazzola, O. V. Vasilyev, and P. Koumoutsakos, “Shape optimization for drag reduction in linked bodies using evolution strategies”, Comput. Struct. vol. 89, no. 11-12, pp. 1224-1231, 2011. Shape optimization for drag reduction in linked bodies using evolution strategiesPublisher’s Version Shape optimization for drag reduction in linked bodies using evolution strategiesPDF
• B. Bayati, P. Chatelain, and P. Koumoutsakos, “Adaptive mesh refinement for stochastic reaction-diffusion processes”, J. Comput. Phys. vol. 230, no. 1, pp. 13-26, 2011. Adaptive mesh refinement for stochastic reaction-diffusion processesPublisher’s Version Adaptive mesh refinement for stochastic reaction-diffusion processesPDF
  

2010

• M. Bergdorf, F. Milde, and P. Koumoutsakos, “Continuum models of mesenchymal cell migration and sprouting angiogenesis“, in Multiscale cancer modeling. CRC Press, 2010, pp. 213–235. Continuum models of mesenchymal cell migration and sprouting angiogenesis PDF
• J. T. Rasmussen, M. M. Hejlesen, A. Larsen, and J. H. Walther, “Discrete vortex method simulations of the aerodynamic admittance in bridge aerodynamics,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 98, no. 12, pp. 754–766, 2010. Discrete vortex method simulations of the aerodynamic admittance in bridge aerodynamicsPublisher’s Version Discrete vortex method simulations of the aerodynamic admittance in bridge aerodynamicsPDF
• E.M. Kotsalis, I. Hanasaki, J.H. Walther, P. Koumoutsakos, “Non-periodic Molecular Dynamics simulations of coarse grained lipid bilayer in water”, in Computers & Mathematics with Applications, Volume 59, Issue 7, April 2010, Pages 2370-2373. Non-periodic Molecular Dynamics simulations of coarse grained lipid bilayer in waterPublisher’s Version Non-periodic Molecular Dynamics simulations of coarse grained lipid bilayer in waterPDF
• D. Rossinelli, B. Hejazialhosseini, M. Bergdorf, and P. Koumoutsakos, “Wavelet-adaptive solvers on multi-core architectures for the simulation of complex systems”, Concurr. Comp.-Prat. E. vol. 23, no. 2, pp. 172-186, 2010. Wavelet-adaptive solvers on multi-core architectures for the simulation of complex systemsPublisher’s Version Wavelet-adaptive solvers on multi-core architectures for the simulation of complex systemsPDF
• D. Rossinelli, M. Bergdorf, G. – H. Cottet, and P. Koumoutsakos, “GPU accelerated simulations of bluff body flows using vortex particle methods”, J. Comput. Phys. vol. 229, no. 9, pp. 3316-3333, 2010. GPU accelerated simulations of bluff body flows using vortex particle methodsPublisher’s Version GPU accelerated simulations of bluff body flows using vortex particle methodsPDF
• G. Morra, D. A. Yuen, L. Boschi, P. Chatelain, P. Koumoutsakos, and P. J. Tackley, “The fate of the slabs interacting with a density/viscosity hill in the mid-mantle”, Phys. Earth Planet. In. vol. 180, no. 3-4, pp. 271-282, 2010. The fate of the slabs interacting with a density/viscosity hill in the mid-mantlePublisher’s Version The fate of the slabs interacting with a density/viscosity hill in the mid-mantlePDF
• B. Hejazialhosseini, D. Rossinelli, M. Bergdorf, and P. Koumoutsakos, “High order finite volume methods on wavelet-adapted grids with local time-stepping on multicore architectures for the simulation of shock-bubble interactions”, J. Comput. Phys. vol. 229, no. 22, pp. 8364-8383, 2010. High order finite volume methods on wavelet-adapted grids with local time-stepping on multicore architectures for the simulation of shock-bubble interactionsPublisher’s Version High order finite volume methods on wavelet-adapted grids with local time-stepping on multicore architectures for the simulation of shock-bubble interactionsPDF
• I. Hanasaki, J. H. Walther, S. Kawano, and P. Koumoutsakos, “Coarse-grained molecular dynamics simulations of shear-induced instabilities of lipid bilayer membranes in water”, Phys. Rev. E, vol. 82, no. 5, 2010. Coarse-grained molecular dynamics simulations of shear-induced instabilities of lipid bilayer membranes in waterPublisher’s Version Coarse-grained molecular dynamics simulations of shear-induced instabilities of lipid bilayer membranes in waterPDF
• P. Chatelain and P. Koumoutsakos, “A Fourier-based elliptic solver for vortical flows with periodic and unbounded directions”, J. Comput. Phys. vol. 229, no. 7, pp. 2425-2431, 2010. A Fourier-based elliptic solver for vortical flows with periodic and unbounded directionsPublisher’s Version A Fourier-based elliptic solver for vortical flows with periodic and unbounded directionsPDF
• B. Bayati, H. Owhadi, and P. Koumoutsakos, “A cutoff phenomenon in accelerated stochastic simulations of chemical kinetics via flow averaging (FLAVOR-SSA)”, J. Chem. Phys. vol. 133, no. 24, pp. 244117, 2010. A cutoff phenomenon in accelerated stochastic simulations of chemical kinetics via flow averaging (FLAVOR-SSA)Publisher’s Version A cutoff phenomenon in accelerated stochastic simulations of chemical kinetics via flow averaging (FLAVOR-SSA)PDF

2009

• Rossinelli, D., Bergdorf, M., Hejazialhosseini, B., Koumoutsakos, P., “Wavelet-Based Adaptive Solvers on Multi-core Architectures for the Simulation of Complex Systems”, in Sips, H., Epema, D., Lin, HX. (eds) Euro-Par 2009 Parallel Processing. Euro-Par 2009, Lecture Notes in Computer Science, vol 5704. Springer, Berlin, Heidelberg. Wavelet-Based Adaptive Solvers on Multi-core Architectures for the Simulation of Complex SystemsPublisher’s Version Wavelet-Based Adaptive Solvers on Multi-core Architectures for the Simulation of Complex SystemsPDF
• Kotsalis, E.M., Walther, J.H., Koumoutsakos, P., “Coupling Atomistic and Continuum Descriptions Using Dynamic Control”, in Ellero, M., Hu, X., Fröhlich, J., Adams, N. (eds) IUTAM Symposium on Advances in Micro- and Nanofluidics. , IUTAM Bookseries, vol 15. Springer, Dordrecht. Coupling Atomistic and Continuum Descriptions Using Dynamic ControlPublisher’s Version Coupling Atomistic and Continuum Descriptions Using Dynamic ControlPDF
• N. Hansen, A. S. P. Niederberger, L. Guzzella, and P. Koumoutsakos, “A Method for Handling Uncertainty in Evolutionary Optimization With an Application to Feedback Control of Combustion,” IEEE T. Evolut. Comput. vol. 13, no. 1, pp. 180–197, 2009. A Method for Handling Uncertainty in Evolutionary Optimization With an Application to Feedback Control of CombustionPublisher’s Version A Method for Handling Uncertainty in Evolutionary Optimization With an Application to Feedback Control of CombustionPDF
• P. Gonnet, J. H. Walther, and P. Koumoutsakos, “vartheta-SHAKE: An extension to SHAKE for the explicit treatment of angular constraints,” Comput. Phys. Commun. vol. 180, no. 3, pp. 360–364, 2009. vartheta-SHAKE: An extension to SHAKE for the explicit treatment of angular constraintsPublisher’s Version vartheta-SHAKE: An extension to SHAKE for the explicit treatment of angular constraintsPDF
• T. Gebäck, M. M. Schulz, P. Koumoutsakos, and M. Detmar, “TScratch: a novel and simple software tool for automated analysis of monolayer wound healing assays.” Biotechniques, vol. 46, no. 4, pp. 265–274, 2009. TScratch: a novel and simple software tool for automated analysis of monolayer wound healing assays.Publisher’s Version TScratch: a novel and simple software tool for automated analysis of monolayer wound healing assays.PDF
• T. Gebäck and P. Koumoutsakos, “Edge detection in microscopy images using curvelets,” BMC Bioinformatics, vol. 10, no. 1, pp. 75,pub-item pub-journals2009. Edge detection in microscopy images using curveletsPublisher’s Version Edge detection in microscopy images using curveletsPDF
• M. Gazzola, C. J. Burckhardt, B. Bayati, M. Engelke, U. F. Greber, and P. Koumoutsakos, “A Stochastic Model for Microtubule Motors Describes the In Vivo Cytoplasmic Transport of Human Adenovirus,” PLoS Comput. Biol. vol. 5, no. 12, pp. e1000623, 2009. A Stochastic Model for Microtubule Motors Describes the In Vivo Cytoplasmic Transport of Human AdenovirusPublisher’s Version A Stochastic Model for Microtubule Motors Describes the In Vivo Cytoplasmic Transport of Human AdenovirusPDF
• B. L. Falcón, et al., “Contrasting Actions of Selective Inhibitors of Angiopoietin-1 and Angiopoietin-2 on the Normalization of Tumor Blood Vessels,” Am. J. Pathol. vol. 175, no. 5, pp. 2159–2170, 2009. Contrasting Actions of Selective Inhibitors of Angiopoietin-1 and Angiopoietin-2 on the Normalization of Tumor Blood VesselsPublisher’s Version Contrasting Actions of Selective Inhibitors of Angiopoietin-1 and Angiopoietin-2 on the Normalization of Tumor Blood VesselsPDF
• H. A. Zambrano, J. H. Walther, P. Koumoutsakos, and I. F. Sbalzarini, “Thermophoretic Motion of Water Nanodroplets Confined inside Carbon Nanotubes,” Nano Lett. vol. 9, no. 1, pp. 66–71, 2009. Thermophoretic Motion of Water Nanodroplets Confined inside Carbon NanotubesPublisher’s Version Thermophoretic Motion of Water Nanodroplets Confined inside Carbon NanotubesPDF
• M. Bergdorf, I. F. Sbalzarini, and P. Koumoutsakos, “A Lagrangian particle method for reaction–diffusion systems on deforming surfaces,” J. Math Biol. vol. 61, no. 5, pp. 649–663, 2009. A Lagrangian particle method for reaction–diffusion systems on deforming surfacesPublisher’s Version A Lagrangian particle method for reaction–diffusion systems on deforming surfacesPDF
• E. Mjolsness, D. Orendorff, P. Chatelain, and P. Koumoutsakos, “An exact accelerated stochastic simulation algorithm,” J. Chem. Phys. vol. 130, no. 14, pp. 144110, 2009. An exact accelerated stochastic simulation algorithmPublisher’s Version An exact accelerated stochastic simulation algorithmPDF
• B. Bayati, P. Chatelain, and P. Koumoutsakos, “D-leaping: Accelerating stochastic simulation algorithms for reactions with delays,” J. Comput. Phys. vol. 228, no. 16, pp. 5908–5916, 2009. D-leaping: Accelerating stochastic simulation algorithms for reactions with delaysPublisher’s Version D-leaping: Accelerating stochastic simulation algorithms for reactions with delaysPDF
• E. M. Kotsalis, J. H. Walther, E. Kaxiras, and P. Koumoutsakos, “Control algorithm for multiscale flow simulations of water,” Phys. Rev. E, vol. 79, no. 4, 2009. Control algorithm for multiscale flow simulations of waterPublisher’s Version Control algorithm for multiscale flow simulations of waterPDF

2008

• P. Koumoutsakos, “Multiscale modeling and simulation for fluid mechanics at the nanoscale“, in Carbon nanotube devices: properties, modeling, integration and applications. Wiley Online Library, 2008, pp. 229–290. Multiscale modeling and simulation for fluid mechanics at the nanoscale DOI Multiscale modeling and simulation for fluid mechanics at the nanoscale BibTex
• Milde, F., Bergdorf, M., Koumoutsakos, P., “A Hybrid Model of Sprouting Angiogenesis”, in Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2008. ICCS 2008, Lecture Notes in Computer Science, vol 5102. Springer, Berlin, Heidelberg. A Hybrid Model of Sprouting AngiogenesisPublisher’s Version A Hybrid Model of Sprouting AngiogenesisPoster A Hybrid Model of Sprouting AngiogenesisPDF
• Kotsalis, E.M., Koumoutsakos, P., “A Control Algorithm for Multiscale Simulations of Liquid Water”, in Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2008. ICCS 2008, Lecture Notes in Computer Science, vol 5102. Springer, Berlin, Heidelberg. A Control Algorithm for Multiscale Simulations of Liquid WaterPublisher’s Version A Control Algorithm for Multiscale Simulations of Liquid WaterPDF
• Hansen, N., Niederberger, A.S.P., Guzzella, L., Koumoutsakos, P., “Evolutionary Optimization of Feedback Controllers for Thermoacoustic Instabilities”, in Morrison, J.F., Birch, D.M., Lavoie, P. (eds) IUTAM Symposium on Flow Control and MEMS, IUTAM Bookseries, vol 7. Springer, Dordrecht. Evolutionary Optimization of Feedback Controllers for Thermoacoustic InstabilitiesPublisher’s Version Evolutionary Optimization of Feedback Controllers for Thermoacoustic InstabilitiesPDF
• Chatelain, P., Curioni, A., Bergdorf, M., Rossinelli, D., Andreoni, W., Koumoutsakos, P. , “Vortex Methods for Massively Parallel Computer Architectures”, in Palma, J.M.L.M., Amestoy, P.R., Daydé, M., Mattoso, M., Lopes, J.C. (eds) High Performance Computing for Computational Science – VECPAR 2008, Lecture Notes in Computer Science, vol 5336. Springer, Berlin, Heidelberg. Vortex Methods for Massively Parallel Computer ArchitecturesPublisher’s Version Vortex Methods for Massively Parallel Computer ArchitecturesPDF
• F. Milde, M. Bergdorf, and P. Koumoutsakos, “A Hybrid Model of Sprouting Angiogenesis,” in Computational Science – ICCS 2008, Springer, 2008, pp. 167–176. A Hybrid Model of Sprouting AngiogenesisPublisher’s Version A Hybrid Model of Sprouting AngiogenesisPoster
• G. Morra, P. Chatelain, P. Tackley, and P. Koumoutsakos, “Earth curvature effects on subduction morphology: Modeling subduction in a spherical setting,” Acta Geotech. vol. 4, no. 2, pp. 95–105, 2008. Earth curvature effects on subduction morphology: Modeling subduction in a spherical settingPublisher’s Version Earth curvature effects on subduction morphology: Modeling subduction in a spherical settingPDF
• F. Milde, M. Bergdorf, and P. Koumoutsakos, “A Hybrid Model for Three-Dimensional Simulations of Sprouting Angiogenesis,” Biophys. J. vol. 95, no. 7, pp. 3146–3160, 2008. A Hybrid Model for Three-Dimensional Simulations of Sprouting AngiogenesisPublisher’s Version A Hybrid Model for Three-Dimensional Simulations of Sprouting AngiogenesisPDF
• S. E. Hieber and P. Koumoutsakos, “A Lagrangian particle method for the simulation of linear and nonlinear elastic models of soft tissue,” J. Comput. Phys. vol. 227, no. 21, pp. 9195–9215, 2008. A Lagrangian particle method for the simulation of linear and nonlinear elastic models of soft tissuePublisher’s Version A Lagrangian particle method for the simulation of linear and nonlinear elastic models of soft tissuePDF
• S. E. Hieber and P. Koumoutsakos, “An immersed boundary method for smoothed particle hydrodynamics of self-propelled swimmers,” J. Comput. Phys. vol. 227, no. 19, pp. 8636–8654, 2008. An immersed boundary method for smoothed particle hydrodynamics of self-propelled swimmersPublisher’s Version An immersed boundary method for smoothed particle hydrodynamics of self-propelled swimmersPDF
• K. Fukagata, S. Kern, P. Chatelain, P. Koumoutsakos, and N. Kasagi, “Evolutionary optimization of an anisotropic compliant surface for turbulent friction drag reduction,” J. Turbul. vol. 9, 2008. Evolutionary optimization of an anisotropic compliant surface for turbulent friction drag reductionPublisher’s Version Evolutionary optimization of an anisotropic compliant surface for turbulent friction drag reductionPDF
• U. Zimmerli and P. Koumoutsakos, “Simulations of Electrophoretic RNA Transport Through Transmembrane Carbon Nanotubes,” Biophys. J. vol. 94, no. 7, pp. 2546–2557, 2008. Simulations of Electrophoretic RNA Transport Through Transmembrane Carbon NanotubesPublisher’s Version Simulations of Electrophoretic RNA Transport Through Transmembrane Carbon NanotubesPDF
• A. Dupuis, P. Chatelain, and P. Koumoutsakos, “An immersed boundary–lattice-Boltzmann method for the simulation of the flow past an impulsively started cylinder,” J. Comput. Phys. vol. 227, no. 9, pp. 4486–4498, 2008. An immersed boundary–lattice-Boltzmann method for the simulation of the flow past an impulsively started cylinderPublisher’s Version An immersed boundary–lattice-Boltzmann method for the simulation of the flow past an impulsively started cylinderPDF
• D. Rossinelli, B. Bayati, and P. Koumoutsakos, “Accelerated stochastic and hybrid methods for spatial simulations of reaction–diffusion systems,” Chemical Phys. Lett. vol. 451, no. 1-3, pp. 136–140, 2008. Accelerated stochastic and hybrid methods for spatial simulations of reaction–diffusion systemsPublisher’s Version Accelerated stochastic and hybrid methods for spatial simulations of reaction–diffusion systemsPDF
• P. Chatelain, A. Curioni, M. Bergdorf, D. Rossinelli, W. Andreoni, and P. Koumoutsakos, “Billion vortex particle direct numerical simulations of aircraft wakes,” Comput. Method. Appl. M. vol. 197, no. 13-16, pp. 1296–1304, 2008. Billion vortex particle direct numerical simulations of aircraft wakesPublisher’s Version Billion vortex particle direct numerical simulations of aircraft wakesPDF
• D. Rossinelli and P. Koumoutsakos, “Vortex methods for incompressible flow simulations on the GPU,” Visual Comput. vol. 24, no. 7-9, pp. 699–708, 2008. Vortex methods for incompressible flow simulations on the GPUPublisher’s Version Vortex methods for incompressible flow simulations on the GPUPDF
• B. Bayati, P. Chatelain, and P. Koumoutsakos, “Multiresolution stochastic simulations of reaction–diffusion processes,” Phys. Chem. Chem. Phys. vol. 10, no. 39, pp. 5963, 2008. Multiresolution stochastic simulations of reaction–diffusion processesPublisher’s Version Multiresolution stochastic simulations of reaction–diffusion processesPDF
• P. Poncet, R. Hildebrand, G. – H. Cottet, and P. Koumoutsakos, “Spatially distributed control for optimal drag reduction of the flow past a circular cylinder,” J. Fluid Mech. vol. 599, 2008. Spatially distributed control for optimal drag reduction of the flow past a circular cylinderPublisher’s Version Spatially distributed control for optimal drag reduction of the flow past a circular cylinderPDF

2007

• G. Morgenthal and J. H. Walther, “An immersed interface method for the vortex-in-cell algorithm,” Computers & Structures, vol. 85, no. 11-14, pp. 712–726, 2007. An immersed interface method for the vortex-in-cell algorithmPublisher’s Version An immersed interface method for the vortex-in-cell algorithmPDF
• G. Morgenthal, J.H. Walther, “An immersed interface method for the Vortex-In-Cell algorithm”, in Computers & Structures, Volume 85, Issues 11–14, June–July 2007, Pages 712-726. An immersed interface method for the Vortex-In-Cell algorithmPublisher’s Version An immersed interface method for the Vortex-In-Cell algorithmPDF
• A. Kotsalis, A. Dupuis, J. Walther, P. Koumoutsakos, “Atomistic-Continuum Simulations of Liquids”, in NSTI-Nanotech 2007, Vol. 1. Atomistic-Continuum Simulations of LiquidsWeb page Atomistic-Continuum Simulations of LiquidsPDF
• E. M. Kotsalis, J. H. Walther, and P. Koumoutsakos, “Control of density fluctuations in atomistic-continuum simulations of dense liquids,” Phys. Rev. E, vol. 76, no. 1, 2007. Control of density fluctuations in atomistic-continuum simulations of dense liquidsPublisher’s Version Control of density fluctuations in atomistic-continuum simulations of dense liquidsPDF
• S. Kern, P. Koumoutsakos, and K. Eschler, “Optimization of anguilliform swimming,” Phys. Fluids, vol. 19, no. 9, pp. 091102, 2007. Optimization of anguilliform swimmingPublisher’s Version Optimization of anguilliform swimmingPDF
• J. A. Helmuth, C. J. Burckhardt, P. Koumoutsakos, U. F. Greber, and I. F. Sbalzarini, “A novel supervised trajectory segmentation algorithm identifies distinct types of human adenovirus motion in host cells,” J. Struct. Biol. vol. 159, no. 3, pp. 347–358, 2007. A novel supervised trajectory segmentation algorithm identifies distinct types of human adenovirus motion in host cellsPublisher’s Version A novel supervised trajectory segmentation algorithm identifies distinct types of human adenovirus motion in host cellsPDF
• A. Dupuis, E. M. Kotsalis, and P. Koumoutsakos, “Coupling lattice Boltzmann and molecular dynamics models for dense fluids,” Phys. Rev. E, vol. 75, no. 4, 2007. Coupling lattice Boltzmann and molecular dynamics models for dense fluidsPublisher’s Version Coupling lattice Boltzmann and molecular dynamics models for dense fluidsPDF
• A. Dupuis and P. Koumoutsakos, “Effects of Atomistic Domain Size on Hybrid Lattice Boltzmann–Molecular Dynamics Simulations of Dense Fluids,” Int. J. Mod. Phys. C, vol. 18, no. 04, pp. 644–651, 2007. Effects of Atomistic Domain Size on Hybrid Lattice Boltzmann–Molecular Dynamics Simulations of Dense FluidsPublisher’s Version Effects of Atomistic Domain Size on Hybrid Lattice Boltzmann–Molecular Dynamics Simulations of Dense FluidsPDF
• P. Chatelain, G. – H. Cottet, and P. Koumoutsakos, “Particle Mesh Hydrodynamics for Astrophysics Simulations,” Int. J. Mod. Phys. C, vol. 18, no. 04, pp. 610–618, 2007. Particle Mesh Hydrodynamics for Astrophysics SimulationsPublisher’s Version Particle Mesh Hydrodynamics for Astrophysics SimulationsPDF
• M. Bergdorf, P. Koumoutsakos, and A. Leonard, “Direct numerical simulations of vortex rings at Re_Gamma= 7500,” J. Fluid Mech. vol. 581, pp. 495–505, 2007. Direct numerical simulations of vortex rings at Re_Gamma= 7500Publisher’s Version Direct numerical simulations of vortex rings at Re_Gamma= 7500PDF
• A. M. Altenhoff, J. H. Walther, and P. Koumoutsakos, “A stochastic boundary forcing for dissipative particle dynamics,” J. Comput. Phys. vol. 225, no. 1, pp. 1125–1136, 2007. A stochastic boundary forcing for dissipative particle dynamicsPublisher’s Version A stochastic boundary forcing for dissipative particle dynamicsPDF
• P. A. E. Schoen, J. H. Walther, D. Poulikakos, and P. Koumoutsakos, “Phonon assisted thermophoretic motion of gold nanoparticles inside carbon nanotubes,” Appl. Phys. Lett. vol. 90, no. 25, pp. 253116, 2007. Phonon assisted thermophoretic motion of gold nanoparticles inside carbon nanotubesPublisher’s Version Phonon assisted thermophoretic motion of gold nanoparticles inside carbon nanotubesPDF

2006

• P. Koumoutsakos and I. Mezic, “Control of fluid flow“, Springer, 2006, pp. 239. Control of fluid flow PDF Control of fluid flow BibTex
• N. Hansen, “An analysis of mutative Σ-self-adaptation on linear fitness functions,” Evolutionary Computation, vol. 14, no. 3, pp. 255–275, 2006. An analysis of mutative Σ-self-adaptation on linear fitness functionsPublisher’s Version An analysis of mutative Σ-self-adaptation on linear fitness functionsPDF
• S. Zimmermann, P. Bauer, R. Held, W. Kinzelbach, and J. H. Walther, “Salt transport on islands in the okavango delta: numerical investigations,” Publisher, vol. 29, no. 1, pp. 11–29, 2006. Salt transport on islands in the okavango delta: numerical investigationsPublisher’s Version Salt transport on islands in the okavango delta: numerical investigationsPDF
• I. F. Sbalzarini, J. H. Walther, B. Polasek, P. Chatelain, M. Bergdorf, S. E. Hieber, E. M. Kotsalis & P. Koumoutsakos, “A Software Framework for the Portable Parallelization of Particle-Mesh Simulations”, in Nagel, W.E., Walter, W.V., Lehner, W. (eds) Euro-Par 2006 Parallel Processing. Euro-Par 2006, Lecture Notes in Computer Science, vol 4128. Springer, Berlin, Heidelberg. A Software Framework for the Portable Parallelization of Particle-Mesh SimulationsPublisher’s Version A Software Framework for the Portable Parallelization of Particle-Mesh SimulationsPDF
• Koumoutsakos, P., Müller, S.D., “Flow Optimization Using Stochastic Algorithms”, in Koumoutsakos, P., Mezic, I. (eds) Control of Fluid Flow, Lecture Notes in Control and Information Sciences, vol 330. Springer, Berlin, Heidelberg. Flow Optimization Using Stochastic AlgorithmsPublisher’s Version Flow Optimization Using Stochastic AlgorithmsPDF
• Kern, S., Hansen, N., Koumoutsakos, P., “Local Meta-models for Optimization Using Evolution Strategies”, in Runarsson, T.P., Beyer, HG., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds) Parallel Problem Solving from Nature – PPSN IX. PPSN 2006, Lecture Notes in Computer Science, vol 4193. Springer, Berlin, Heidelberg. Local Meta-models for Optimization Using Evolution StrategiesPublisher’s Version Local Meta-models for Optimization Using Evolution StrategiesPDF
• Hansen, N., Gemperle, F., Auger, A., Koumoutsakos, P., “When Do Heavy-Tail Distributions Help?”, in Runarsson, T.P., Beyer, HG., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds) Parallel Problem Solving from Nature – PPSN IX. PPSN 2006, Lecture Notes in Computer Science, vol 4193. Springer, Berlin, Heidelberg. When Do Heavy-Tail Distributions Help?Publisher’s Version When Do Heavy-Tail Distributions Help?PDF
• N. Hansen, “An analysis of mutative Σ-self-adaptation on linear fitness functions,” Envol. Comput. vol. 14, no. 3, pp. 255–275, 2006. An analysis of mutative Σ-self-adaptation on linear fitness functionsPublisher’s Version An analysis of mutative Σ-self-adaptation on linear fitness functionsPDF
• P. A. E. Schoen, J. H. Walther, S. Arcidiacono, D. Poulikakos, and P. Koumoutsakos, “Nanoparticle Traffic on Helical Tracks: Thermophoretic Mass Transport through Carbon Nanotubes,” Nano Lett. vol. 6, no. 9, pp. 1910–1917, 2006. Nanoparticle Traffic on Helical Tracks: Thermophoretic Mass Transport through Carbon NanotubesPublisher’s Version Nanoparticle Traffic on Helical Tracks: Thermophoretic Mass Transport through Carbon NanotubesPDF
• I. F. Sbalzarini, J. H. Walther, M. Bergdorf, S. E. Hieber, E. M. Kotsalis, and P. Koumoutsakos, “PPM – A highly efficient parallel particle–mesh library for the simulation of continuum systems,” J. Comput. Phys. vol. 215, no. 2, pp. 566–588, 2006. PPM – A highly efficient parallel particle–mesh library for the simulation of continuum systemsPublisher’s Version PPM – A highly efficient parallel particle–mesh library for the simulation of continuum systemsPDF
• I. F. Sbalzarini, A. Hayer, A. Helenius, and P. Koumoutsakos, “Simulations of (An)Isotropic Diffusion on Curved Biological Surfaces,” Biophys. J. vol. 90, no. 3, pp. 878–885, 2006. Simulations of (An)Isotropic Diffusion on Curved Biological SurfacesPublisher’s Version Simulations of (An)Isotropic Diffusion on Curved Biological SurfacesPDF
• S. Kern and P. Koumoutsakos, “Simulations of optimized anguilliform swimming,” J. Exp. Biol. vol. 209, no. 24, pp. 4841–4857, 2006. Simulations of optimized anguilliform swimmingPublisher’s Version Simulations of optimized anguilliform swimmingPDF
• K. Fukagata, N. Kasagi, and P. Koumoutsakos, “A theoretical prediction of friction drag reduction in turbulent flow by superhydrophobic surfaces,” Phys. Fluids, vol. 18, no. 5, pp. 051703, 2006. A theoretical prediction of friction drag reduction in turbulent flow by superhydrophobic surfacesPublisher’s Version A theoretical prediction of friction drag reduction in turbulent flow by superhydrophobic surfacesPDF
• M. Bergdorf and P. Koumoutsakos, “A Lagrangian Particle-Wavelet Method,” Multiscale Model. Sim. vol. 5, no. 3, pp. 980–995, 2006. A Lagrangian Particle-Wavelet MethodPublisher’s Version A Lagrangian Particle-Wavelet MethodPDF
• A. Auger, P. Chatelain, and P. Koumoutsakos, “R-leaping: Accelerating the stochastic simulation algorithm by reaction leaps,” J. Chem. Phys. vol. 125, no. 8, pp. 084103, 2006. R-leaping: Accelerating the stochastic simulation algorithm by reaction leapsPublisher’s Version R-leaping: Accelerating the stochastic simulation algorithm by reaction leapsPDF

2005

• T. Hou and P. Koumoutsakos, “Special section on multiscale modeling in materials and life sciences“, in Multiscale modeling & simulation. Society for Industrial & Applied Mathematics (SIAM), 2005, vol. 4, pp. 213–214. Special section on multiscale modeling in materials and life sciences PDF
• C. Luedeke, S. B. Frei, I. Sbalzarini, H. Schwarz, A. Spang, and Y. Barral, “Septin-dependent compartmentalization of the endoplasmic reticulum during yeast polarized growth,” The Journal of Cell Biology, vol. 169, no. 6, pp. 897–908, 2005. Septin-dependent compartmentalization of the endoplasmic reticulum during yeast polarized growthPublisher’s Version Septin-dependent compartmentalization of the endoplasmic reticulum during yeast polarized growthPDF
• P. Poncet and P. Koumoutsakos, “Optimization of Vortex Shedding In 3-D Wakes Using Belt Actuators,” Int. J. Offshore Polar, vol. 15, no. 1, pp. 7–13, 2005. Optimization of Vortex Shedding In 3-D Wakes Using Belt ActuatorsPublisher’s Version Optimization of Vortex Shedding In 3-D Wakes Using Belt ActuatorsPDF
• P. Koumoutsakos, “Multicscale Flow Simulations using Particles,” Annu. Rev. Fluid Mech. vol. 37, no. 1, pp. 457–487, 2005. Multicscale Flow Simulations using ParticlesPublisher’s Version Multicscale Flow Simulations using ParticlesPDF
• E. M. Kotsalis, E. Demosthenous, J. H. Walther, S. C. Kassinos, and P. Koumoutsakos, “Wetting of doped carbon nanotubes by water droplets,” Chem. Phys. Lett. vol. 412, no. 4–6, pp. 250–254, 2005. Wetting of doped carbon nanotubes by water dropletsPublisher’s Version Wetting of doped carbon nanotubes by water dropletsPDF
• S. E. Hieber and P. Koumoutsakos, “A Lagrangian particle level set method,” J. Comput. Phys. vol. 210, no. 1, pp. 342–367, 2005. A Lagrangian particle level set methodPublisher’s Version A Lagrangian particle level set methodPDF
• U. Zimmerli, P. G. Gonnet, J. H. Walther, and P. Koumoutsakos, “Curvature Induced L-Defects in Water Conduction in Carbon Nanotubes,” Nano Lett. vol. 5, no. 6, pp. 1017–1022, 2005. Curvature Induced L-Defects in Water Conduction in Carbon NanotubesPublisher’s Version Curvature Induced L-Defects in Water Conduction in Carbon NanotubesPDF
• H. Ewers, A. E. Smith, I. F. Sbalzarini, H. Lilie, P. Koumoutsakos, and A. Helenius, “Single-particle tracking of murine polyoma virus-like particles on live cells and artificial membranes,” P. Natl. Acad. Sci. vol. 102, no. 42, pp. 15110–15115, 2005. Single-particle tracking of murine polyoma virus-like particles on live cells and artificial membranesPublisher’s Version Single-particle tracking of murine polyoma virus-like particles on live cells and artificial membranesPDF
• T. Werder, J. H. Walther, and P. Koumoutsakos, “Hybrid atomistic–continuum method for the simulation of dense fluid flows,” J. Comput. Phys. vol. 205, no. 1, pp. 373–390, 2005. Hybrid atomistic–continuum method for the simulation of dense fluid flowsPublisher’s Version Hybrid atomistic–continuum method for the simulation of dense fluid flowsPDF
• D. Bueche, N. N. Schraudolph, and P. Koumoutsakos, “Accelerating Evolutionary Algorithms With Gaussian Process Fitness Function Models,” IEEE T. Syst. Man Cy. C, vol. 35, no. 2, pp. 183–194, 2005. Accelerating Evolutionary Algorithms With Gaussian Process Fitness Function ModelsPublisher’s Version Accelerating Evolutionary Algorithms With Gaussian Process Fitness Function ModelsPDF
• I. F. Sbalzarini and P. Koumoutsakos, “Feature point tracking and trajectory analysis for video imaging in cell biology,” J. Struct. Biol. vol. 151, no. 2, pp. 182–195, 2005. Feature point tracking and trajectory analysis for video imaging in cell biologyPublisher’s Version Feature point tracking and trajectory analysis for video imaging in cell biologyPDF
• M. Bergdorf, G. – H. Cottet, and P. Koumoutsakos, “Multilevel Adaptive Particle Methods for Convection-Diffusion Equations,” Multiscale Model. Sim. vol. 4, no. 1, pp. 328–357, 2005. Multilevel Adaptive Particle Methods for Convection-Diffusion EquationsPublisher’s Version Multilevel Adaptive Particle Methods for Convection-Diffusion EquationsPDF
• I. F. Sbalzarini, A. Mezzacasa, A. Helenius, and P. Koumoutsakos, “Effects of Organelle Shape on Fluorescence Recovery after Photobleaching,” Biophys. J. vol. 89, no. 3, pp. 1482–1492, 2005. Effects of Organelle Shape on Fluorescence Recovery after PhotobleachingPublisher’s Version Effects of Organelle Shape on Fluorescence Recovery after PhotobleachingPDF
• S. Arcidiacono, J. H. Walther, D. Poulikakos, D. Passerone, and P. Koumoutsakos, “Solidification of Gold Nanoparticles in Carbon Nanotubes,” Phys. Rev. Lett. vol. 94, no. 10, 2005. Solidification of Gold Nanoparticles in Carbon NanotubesPublisher’s Version Solidification of Gold Nanoparticles in Carbon NanotubesPDF
• P. Poncet, G. – H. Cottet, and P. Koumoutsakos, “Control of three-dimensional wakes using evolution strategies,” C.R. Mecanique, vol. 333, no. 1, pp. 65–77, 2005. Control of three-dimensional wakes using evolution strategiesPublisher’s Version Control of three-dimensional wakes using evolution strategiesPDF

2004

• S. Attinger and P. D. Koumoutsakos, “Multiscale modelling and simulation“, Springer, 2004, pp. 292. Multiscale modelling and simulation PDF Multiscale modelling and simulation BibTex
• F. Lisacek, et al., “Shaping biological knowledge: applications in proteomics,” Comparative and Functional Genomics, vol. 5, no. 2, pp. 190–195, 2014. Shaping biological knowledge: applications in proteomicsPublisher’s Version Shaping biological knowledge: applications in proteomicsPDF
• P. Gonnet, K. E. Rudd, and F. Lisacek, “Fine-tuning the prediction of sequences cleaved by signal peptidase II: a curated set of proven and predicted lipoproteins ofEscherichia coli k-12,” Proteomics, vol. 4, no. 6, pp. 1597–1613, 2004. Fine-tuning the prediction of sequences cleaved by signal peptidase II: a curated set of proven and predicted lipoproteins ofEscherichia coli k-12Publisher’s Version Fine-tuning the prediction of sequences cleaved by signal peptidase II: a curated set of proven and predicted lipoproteins ofEscherichia coli k-12PDF
• P. Poncet, P. Koumoutsakos, “Optimization of vortex shedding in 3-D wakes using belt actuators”, in International Journal of Offshore and Polar Engineering, 15:7-13. Optimization of vortex shedding in 3-D wakes using belt actuatorsPublisher’s Version Optimization of vortex shedding in 3-D wakes using belt actuatorsPDF
• Ocenasek, J., Kern, S., Hansen, N., Koumoutsakos, P. , “A Mixed Bayesian Optimization Algorithm with Variance Adaptation”, in Yao, X., et al. Parallel Problem Solving from Nature – PPSN VIII. PPSN 2004, Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. A Mixed Bayesian Optimization Algorithm with Variance AdaptationPublisher’s Version A Mixed Bayesian Optimization Algorithm with Variance AdaptationPDF
• Jaffe, R.d L., Walther, J. H., Zimmerli, U., Koumoutsakos, P., “Modeling the interaction Between Ethylene Diamine and Water Films on the Surface of a Carbon Nanotube”, in Electrochemical Society 2004th Meeting. Modeling the interaction Between Ethylene Diamine and Water Films on the Surface of a Carbon NanotubePublisher’s Version Modeling the interaction Between Ethylene Diamine and Water Films on the Surface of a Carbon NanotubePDF
• Hansen, N., Kern, S., “Evaluating the CMA Evolution Strategy on Multimodal Test Functions”, in Yao, X., et al. Parallel Problem Solving from Nature – PPSN VIII. PPSN 2004, Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. Evaluating the CMA Evolution Strategy on Multimodal Test FunctionsPublisher’s Version Evaluating the CMA Evolution Strategy on Multimodal Test FunctionsPDF
• P. Gonnet, U. Zimmerli, J.H. Walther, T. Werder and P. Koumoutsakos, “Wetting and hydrophobicity of nanoscale systems with impurities”, in NSTI-Nanotech 2004, Vol. 3, Wetting and hydrophobicity of nanoscale systems with impuritiesPublisher’s Version Wetting and hydrophobicity of nanoscale systems with impuritiesPDF
• T. Werder, J. H. Walther, E. Kotsalis, P. Gonnet and P. Koumoutsakos, “Hybrid atomistic-continuum fluid mechanics”, in NSTI-Nanotech 2004, Vol. 3, Hybrid atomistic-continuum fluid mechanicsPublisher’s Version Hybrid atomistic-continuum fluid mechanicsPDF
• J. H. Walther, T. Werder, R. L. Jaffe, and P. Koumoutsakos, “Hydrodynamic properties of carbon nanotubes,” Phys. Rev. E, vol. 69, no. 6, 2004. Hydrodynamic properties of carbon nanotubesPublisher’s Version Hydrodynamic properties of carbon nanotubesPDF
• J. H. Walther, et al., “Water–carbon interactions III: The influence of surface and fluid impurities,” Phys. Chem. Chem. Phys. vol. 6, no. 8, pp. 1988–1995, 2004. Water–carbon interactions III: The influence of surface and fluid impuritiesPublisher’s Version Water–carbon interactions III: The influence of surface and fluid impuritiesPDF
• S. D. Müller, I. Mezić, J. H. Walther, and P. Koumoutsakos, “Transverse momentum micromixer optimization with evolution strategies,” Comput. Fluids, vol. 33, no. 4, pp. 521–531, 2004. Transverse momentum micromixer optimization with evolution strategiesPublisher’s Version Transverse momentum micromixer optimization with evolution strategiesPDF
• M. Milano, P. Koumoutsakos, and J. Schmidhuber, “Self-Organizing Nets for Optimization,” IEEE T. Neural Networ. vol. 15, no. 3, pp. 758–765, 2004. Self-Organizing Nets for OptimizationPublisher’s Version Self-Organizing Nets for OptimizationPDF
• U. Zimmerli, M. Parrinello, and P. Koumoutsakos, “Dispersion corrections to density functionals for water aromatic interactions,” J. Chem. Phys. vol. 120, no. 6, pp. 2693–2699, 2004. Dispersion corrections to density functionals for water aromatic interactionsPublisher’s Version Dispersion corrections to density functionals for water aromatic interactionsPDF
• J. H. Walther, R. L. Jaffe, E. M. Kotsalis, T. Werder, T. Halicioglu, and P. Koumoutsakos, “Hydrophobic hydration of C60 and carbon nanotubes in water,” Carbon, vol. 42, no. 5–6, pp. 1185–1194, 2004. Hydrophobic hydration of C60 and carbon nanotubes in waterPublisher’s Version Hydrophobic hydration of C60 and carbon nanotubes in waterPDF
• E. M. Kotsalis, J. H. Walther, and P. Koumoutsakos, “Multiphase water flow inside carbon nanotubes,” Int. J. Multiphas. Flow, vol. 30, no. 7–8, pp. 995–1010, 2004. Multiphase water flow inside carbon nanotubesPublisher’s Version Multiphase water flow inside carbon nanotubesPDF
• S. Kern, S. D. Müller, N. Hansen, D. Büche, J. Ocenasek, and P. Koumoutsakos, “Learning probability distributions in continuous evolutionary algorithms – a comparative review,” Nat. Comput., vol. 3, no. 1, pp. 77–112, 2004. Learning probability distributions in continuous evolutionary algorithms – a comparative reviewPublisher’s Version Learning probability distributions in continuous evolutionary algorithms – a comparative reviewPDF
• R. L. Jaffe, P. Gonnet, T. Werder, J. H. Walther, and P. Koumoutsakos, “Water–Carbon Interactions 2: Calibration of Potentials using Contact Angle Data for Different Interaction Models,” Mol. Simulat., vol. 30, no. 4, pp. 205–216, 2004. Water–Carbon Interactions 2: Calibration of Potentials using Contact Angle Data for Different Interaction ModelsPublisher’s Version Water–Carbon Interactions 2: Calibration of Potentials using Contact Angle Data for Different Interaction ModelsPDF
• S. E. Hieber, J. H. Walther, and P. Koumoutsakos, “Remeshed smoothed particle hydrodynamics simulation of the mechanical behavior of human organs,” Technol. Health Care, vol. 12, no. 4, pp. 305–314, 2004. Remeshed smoothed particle hydrodynamics simulation of the mechanical behavior of human organsPublisher’s Version Remeshed smoothed particle hydrodynamics simulation of the mechanical behavior of human organsPDF

2003

• O. A. Cirpka and S. Attinger, “Effective dispersion in heterogeneous media under random transient flow conditions,” Water Resources Research, vol. 39, no. 9, 2003. Effective dispersion in heterogeneous media under random transient flow conditionsPublisher’s Version Effective dispersion in heterogeneous media under random transient flow conditionsPDF
• M. Bergdorf, C. Peter, and P. H. Hünenberger, “Influence of cut-off truncation and artificial periodicity of electrostatic interactions in molecular simulations of solvated ions: a continuum electrostatics study,” The Journal of Chemical Physics, vol. 119, no. 17, pp. 9129–9144, 2003. Influence of cut-off truncation and artificial periodicity of electrostatic interactions in molecular simulations of solvated ions: a continuum electrostatics studyPublisher’s Version Influence of cut-off truncation and artificial periodicity of electrostatic interactions in molecular simulations of solvated ions: a continuum electrostatics studyPDF
• J. H. Walther, “An influence matrix particle–particle particle-mesh algorithm with exact particle–particle correction,” The Journal of Chemical Physics, vol. 184, no. 2, pp. 670–678, 2003. An influence matrix particle–particle particle-mesh algorithm with exact particle–particle correctionPublisher’s Version An influence matrix particle–particle particle-mesh algorithm with exact particle–particle correctionPDF
• F. A. Gers, N. N. Schraudolph, and J. Schmidhuber, “Learning precise timing with lstm recurrent networks,” Journal of Machine Learning Research, vol. 3, pp. 115–143, 2003. Learning precise timing with lstm recurrent networksPublisher’s Version Learning precise timing with lstm recurrent networksPDF
• Werder T., W. J. Honorécc, Jaffe R.L., Koumoutsakos P., “Water-carbon interactions, Potential energy calibration using experimental data”, in Nanotech 2003 : 2003 Nanotechnology Conference and Trade Show; February 23 – 27, 2003, San Francisco, California, USA, Vol. 3 pp. 546-548. Water-carbon interactions, Potential energy calibration using experimental dataPublisher’s Version Water-carbon interactions, Potential energy calibration using experimental dataPDF
• C. O. Paschereit, B. Schuermans, D. Buche, “Combustion Process Optimization Using Evolutionary Algorithm”, in ASME Turbo Expo 2003, Paper No: GT2003-38393, pp. 281-291; 11 pages. Combustion Process Optimization Using Evolutionary AlgorithmPublisher’s Version Combustion Process Optimization Using Evolutionary AlgorithmPDF
• Koumoutsakos P., Jaffe R.l., Werder T., Walther J.H., “On the Validity of the No-Slip Condition in Nanofluidics”, in Technical Proceedings of the 2003 Nanotechnology Conference and Trade Show, Volume 1, 148-151 On the Validity of the No-Slip Condition in NanofluidicsPublisher’s Version On the Validity of the No-Slip Condition in NanofluidicsPDF
• Büche, D., Müller, S., Koumoutsakos, P., “Self-Adaptation for Multi-objective Evolutionary Algorithms”, in Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003, Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. Self-Adaptation for Multi-objective Evolutionary AlgorithmsPublisher’s Version Self-Adaptation for Multi-objective Evolutionary AlgorithmsPDF
• D. B ̈uche, N. N. Schraudolph, P. Koumoutsakos, “Accelerating Evolutionary Algorithms Using Fitness Function Models” Accelerating Evolutionary Algorithms Using Fitness Function ModelsPDF
• D. Büche, “Automated Design Optimization Of Compressor Blades For Stationary, Large-Scale Turbomachinery”, in Proceedings of IGTI03 ASME Turbo Expo 2003: Power for Land, Sea and Air. Automated Design Optimization Of Compressor Blades For Stationary, Large-Scale TurbomachineryPublisher’s Version Automated Design Optimization Of Compressor Blades For Stationary, Large-Scale TurbomachineryPDF
• T. Werder, J. H. Walther, R. L. Jaffe, T. Halicioglu, and P. Koumoutsakos, “On the Water-Carbon Interaction for Use in Molecular Dynamics Simulations of Graphite and Carbon Nanotubes,” J. Phys. Chem. B, vol. 112, no. 44, pp. 14090–14090, 2003. On the Water-Carbon Interaction for Use in Molecular Dynamics Simulations of Graphite and Carbon NanotubesPublisher’s Version On the Water-Carbon Interaction for Use in Molecular Dynamics Simulations of Graphite and Carbon NanotubesPDF
• N. Hansen, S. D. Müller, and P. Koumoutsakos, “Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES),” Evol. Comput. vol. 11, no. 1, pp. 1–18, 2003. Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES)Publisher’s Version Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES)PDF

2002

• E. Meiburg, G. Cottet, A. Ghoniem, and P. Koumoutsakos, “Proceedings of the fourth international workshop on vortex flows and related numerical methods“, in IOP Publishing Ltd, 2002. Proceedings of the fourth international workshop on vortex flows and related numerical methods BibTex
• R. Herbrich and T. Graepel, “A PAC-Bayesian margin bound for linear classifiers,” IEEE Transactions on Information Theory, vol. 48, no. 12, pp. 3140–3150, 2002. A PAC-Bayesian margin bound for linear classifiersPublisher’s Version A PAC-Bayesian margin bound for linear classifiersPDF
• M. Dentz, H. Kinzelbach, S. Attinger, and W. Kinzelbach, “Temporal behavior of a solute cloud in a heterogeneous porous medium 3. numerical simulations,” Water Resources Research, vol. 38, no. 7, pp. 23–1–23–13, 2002. Temporal behavior of a solute cloud in a heterogeneous porous medium 3. numerical simulationsPublisher’s Version Temporal behavior of a solute cloud in a heterogeneous porous medium 3. numerical simulationsPDF
• F. Stauffer, S. Attinger, S. Zimmermann, and W. Kinzelbach, “Uncertainty estimation of well catchments in heterogeneous aquifers,” Water Resources Research, vol. 38, no. 11, pp. 20–1–20–8, 2002. Uncertainty estimation of well catchments in heterogeneous aquifersPublisher’s Version Uncertainty estimation of well catchments in heterogeneous aquifersPDF
• I. Lunati, S. Attinger, and W. Kinzelbach, “Macrodispersivity for transport in arbitrary nonuniform flow fields: asymptotic and preasymptotic results,” Water Resources Research, vol. 38, no. 10, pp. 5–1–5–11, 2002. Macrodispersivity for transport in arbitrary nonuniform flow fields: asymptotic and preasymptotic resultsPublisher’s Version Macrodispersivity for transport in arbitrary nonuniform flow fields: asymptotic and preasymptotic resultsPDF
• S. Attinger, J. Eberhard, and N. Neuss, “Filtering procedures for flow in heterogeneous porous media: numerical results,” Computing and Visualization in Science, vol. 5, no. 2, pp. 67–72, 2002. Filtering procedures for flow in heterogeneous porous media: numerical resultsPublisher’s Version Filtering procedures for flow in heterogeneous porous media: numerical resultsPDF
• J. H. Walther and G. Morgenthal, “An immersed interface method for the vortex-in-cell algorithm,” Journal of Turbulence, vol. 3, pp. N39, 2002. An immersed interface method for the vortex-in-cell algorithmPublisher’s Version An immersed interface method for the vortex-in-cell algorithmPDF
• P. Gonnet and F. Lisacek, “Probabilistic alignment of motifs with sequences,” Bioinformatics, vol. 18, no. 8, pp. 1091–1101, 2002. Probabilistic alignment of motifs with sequencesPublisher’s Version Probabilistic alignment of motifs with sequencesPDF
• N. N. Schraudolph, “Fast curvature matrix-vector products for second-order gradient descent,” Neural Computation, vol. 14, no. 7, pp. 1723–1738, 2002. Fast curvature matrix-vector products for second-order gradient descentPublisher’s Version Fast curvature matrix-vector products for second-order gradient descentPDF
• P. Yi, D. Poulikakos, J. Walther, and G. Yadigaroglu, “Molecular dynamics simulation of vaporization of an ultra-thin liquid argon layer on a surface,” International Journal of Heat and Mass Transfer, vol. 45, no. 10, pp. 2087–2100, 2002. Molecular dynamics simulation of vaporization of an ultra-thin liquid argon layer on a surfacePublisher’s Version Molecular dynamics simulation of vaporization of an ultra-thin liquid argon layer on a surfacePDF
• S. Attinger, “Parameterizing macro-dispersivity: first pick a length scale,” Hydrological Processes, vol. 16, no. 8, pp. 1685–1687, 2002. Parameterizing macro-dispersivity: first pick a length scalePublisher’s Version Parameterizing macro-dispersivity: first pick a length scalePDF
• T. Werder, J. H. Walther and P. Koumoutsakos, “Hydrodynamics of Carbon Nanotubes – Contact Angle and Hydrophobic Hydration”. Hydrodynamics of Carbon Nanotubes - Contact Angle and Hydrophobic HydrationPublisher’s Version Hydrodynamics of Carbon Nanotubes - Contact Angle and Hydrophobic HydrationPDF
• H. Esmaeilzadeh, J. Su, M. Charmchi, H. Sun, “Effect of hydrophobicity on the water flow in carbon nanotube—A molecular dynamics study”. Effect of hydrophobicity on the water flow in carbon nanotube—A molecular dynamics studyPublisher’s Version Effect of hydrophobicity on the water flow in carbon nanotube—A molecular dynamics studyPDF
• Walther J. H., Jaffe R. L., Werder T., Halicioglu T., Koumoutsakos P., “On the Boundary Condition for Water at a Hydrophobic, Dense Surface”, in Studying Turbulence Using Numerical Simulation Databases – IX: Proceedings of the 2002 Summer Program. On the Boundary Condition for Water at a Hydrophobic, Dense SurfacePublisher’s Version On the Boundary Condition for Water at a Hydrophobic, Dense SurfacePDF
• Schraudolph, N.N., Graepel, T., “Conjugate Directions for Stochastic Gradient Descent”, in Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002, Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. Conjugate Directions for Stochastic Gradient Descent Publisher’s Version Conjugate Directions for Stochastic Gradient Descent PDF
• Sbalzarini I. F., Theriot J., Koumoutsakos P., “Machine Learning for Biological Trajectory Classification Applications”, in Studying Turbulence Using Numerical Simulation Databases – IX: Proceedings of the 2002 Summer Program. Machine Learning for Biological Trajectory Classification ApplicationsPublisher’s Version Machine Learning for Biological Trajectory Classification ApplicationsPDF
• S.D. Muller, N.N. Schraudolph, P.D. Koumoutsakos, “Step size adaptation in evolution strategies using reinforcement learning”, in Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No.02TH8600). Step size adaptation in evolution strategies using reinforcement learningPublisher’s Version Step size adaptation in evolution strategies using reinforcement learningPDF
• Müller, S.D., Koumoutsakos, P., “Control of Micromixers, Jets, and Turbine Cooling using Evolution Strategies”, in Karagozian, A.R., Cortelezzi, L., Soldati, A. (eds) Manipulation and Control of Jets in Crossflow, International Centre for Mechanical Sciences, vol 439. Springer, Vienna. Control of Micromixers, Jets, and Turbine Cooling using Evolution StrategiesPublisher’s Version Control of Micromixers, Jets, and Turbine Cooling using Evolution StrategiesPDF
• Müller, S.D., Hansen, N., Koumoutsakos, P. , “Increasing the Serial and the Parallel Performance of the CMA-Evolution Strategy with Large Populations”, in Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002, Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. Increasing the Serial and the Parallel Performance of the CMA-Evolution Strategy with Large PopulationsPublisher’s Version Increasing the Serial and the Parallel Performance of the CMA-Evolution Strategy with Large PopulationsPDF
• S. Müller, N. N. Schraudolph, P. Koumoutsakos, and N. Hansen, “Step Size Adaptation in Evolution Strategies – Two Approaches”, in Genetic and Evolutionary Computation Conference Workshop Program, pp. 161–164, AAAI, New York, 2002. Step Size Adaptation in Evolution Strategies - Two ApproachesPublisher’s Version Step Size Adaptation in Evolution Strategies - Two ApproachesPDF
• T. Graepel, “Kernel Matrix Completion by Semidefinite Programming”, in ICANN ’02: Proceedings of the International Conference on Artificial Neural Networks, Pages 694 – 699. Kernel Matrix Completion by Semidefinite ProgrammingPublisher’s Version Kernel Matrix Completion by Semidefinite ProgrammingPDF
• Graepel, T., Schraudolph, N.N., “Stable Adaptive Momentum for Rapid Online Learning in Nonlinear Systems”, in Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002, Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. Stable Adaptive Momentum for Rapid Online Learning in Nonlinear SystemsPublisher’s Version Stable Adaptive Momentum for Rapid Online Learning in Nonlinear SystemsPDF
• R. Gamperle, S. D. Muller, P. Koumoutsakos, “A Parameter Study for Differential Evolution”, in Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, 2002, pp. 293–298. A Parameter Study for Differential EvolutionPublisher’s Version A Parameter Study for Differential EvolutionPDF
• P. Catalanot, M. Wang, G. laccarino, Ivo F. Sbalzarinit, and P. Koumoutsakos, “Optimization of cylinder flow control via zero net mass flux actuators”, in Center for Turbulence Research Proceedings of the Summer Program 2002. Optimization of cylinder flow control via zero net mass flux actuatorsPublisher’s Version Optimization of cylinder flow control via zero net mass flux actuatorsPDF
• Büche, D., Guidati, G., Stoll, P., Koumoutsakos, P., “Self-Organizing Maps for Pareto Optimization of Airfoils”, in Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002, Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. Self-Organizing Maps for Pareto Optimization of AirfoilsPublisher’s Version Self-Organizing Maps for Pareto Optimization of AirfoilsPDF
• D. Buche, M. Milano, P. Koumoutsakos, “Self-Organizing Maps for Multi-Objective Optimization”, in 4th Annual Conference on Genetic and Evolutionary Computation – GECCO ’02, 2002. Self-Organizing Maps for Multi-Objective OptimizationPublisher’s Version Self-Organizing Maps for Multi-Objective OptimizationPDF

2001

• T. Werder, K. Gerdes, D. Schötzau, and C. Schwab, “Hp-discontinuous galerkin time stepping for parabolic problems,” Computer Methods in Applied Mechanics and Engineering, vol. 190, no. 49-50, pp. 6685–6708, 2001. Hp-discontinuous galerkin time stepping for parabolic problemsPublisher’s Version Hp-discontinuous galerkin time stepping for parabolic problemsPDF
• M. Klapper-Rybicka, N. N. Schraudolph, and J. Schmidhuber, “Unsupervised learning in LSTM recurrent neural networks,” ICANN ’01: Proceedings of the International Conference on Artificial Neural Networks, pp. 684–691, 2001. Unsupervised learning in LSTM recurrent neural networksPublisher’s Version Unsupervised learning in LSTM recurrent neural networksPDF
• N. Hansen and A. Ostermeier, “Completely derandomized self-adaptation in evolution strategies,” Evolutionary Computation, vol. 9, no. 2, pp. 159–195, 2001. Completely derandomized self-adaptation in evolution strategiesPublisher’s Version Completely derandomized self-adaptation in evolution strategiesPDF
• D. Bueche and R. Dornberger, “New Evolutionary Algorithm for Multi-objective Optimization and its Application to Engineering Design Problems”, in Proceedings of the Fourth World Congress of Structural and Multidisciplinary Optimization, 2001. New Evolutionary Algorithm for Multi-objective Optimization and its Application to Engineering Design ProblemsPDF
• M. Klapper-Rybicka, N. N. Schraudolph, J. Schmidhuber, Unsupervised Learning in LSTM Recurrent Neural Networks”. Unsupervised Learning in LSTM Recurrent Neural NetworksPublisher’s Version Unsupervised Learning in LSTM Recurrent Neural NetworksPDF
• E. M. Kotsalis, R. L. Jaffe, J. H. Walther, T. Werder and P. Koumoutsakos, “Buckyballs in water: structural characteristics and energetics”, in Center for Turbulence Research Annual Research Briefs 2001 Buckyballs in water: structural characteristics and energeticsPublisher’s Version Buckyballs in water: structural characteristics and energeticsPDF
• P. Koumoutsakos, T.Werder, J. Walther, R. Jaffe and T. Halicioglu, “Carbon Nanotubes in Water: MD Simulations of Internal & External Flow, Self Organization”, in Meeting: 9th Conference on Molecular Nanotechnology. Carbon Nanotubes in Water: MD Simulations of Internal & External Flow, Self OrganizationPublisher’s Version Carbon Nanotubes in Water: MD Simulations of Internal & External Flow, Self OrganizationPDF
• Milano, M., Schmidhuber, J., Koumoutsakos, P., “Active Learning with Adaptive Grids”, in Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. Active Learning with Adaptive GridsPublisher’s Version Active Learning with Adaptive GridsPDF
• S. D. Müller, P. D. Koumoutsakos, “Mixing Optimization With Evolution Strategies”, in Evolutionary Methods For Design, Optimisation And Control. Mixing Optimization With Evolution StrategiesPDF
• S.D. Muller; I.F. Sbalzarini; J.H. Walther; P.D. Koumoutsakos, “Evolution strategies for the optimization of microdevices”, in Proceedings of the 2001 Congress on Evolutionary Computation, (IEEE Cat. No.01TH8546) Evolution strategies for the optimization of microdevicesPublisher’s Version Evolution strategies for the optimization of microdevicesPDF
• Ivo F. Sbalzarini, S. D. Miiller, P. Koumoutsakos, G.-H Cottet, “Evolution Strategies for Computational and Experimental Fluid Dynamic Applications”. Evolution Strategies for Computational and Experimental Fluid Dynamic ApplicationsPublisher’s Version Evolution Strategies for Computational and Experimental Fluid Dynamic ApplicationsPDF
• Sbalzarini, I.F., Müller, S., Koumoutsakos, P., “Microchannel Optimization Using Multiobjective Evolution Strategies”, in Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. Microchannel Optimization Using Multiobjective Evolution StrategiesPublisher’s Version Microchannel Optimization Using Multiobjective Evolution StrategiesPDF
• Schraudolph, N.N., “Fast Curvature Matrix-Vector Products”, in Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. Fast Curvature Matrix-Vector ProductsPublisher’s Version Fast Curvature Matrix-Vector ProductsPDF
• N. Hansen and A. Ostermeier, “Completely derandomized self-adaptation in evolution strategies,” Evol. Comput. vol. 9, no. 2, pp. 159–195, 2001. Completely derandomized self-adaptation in evolution strategiesPublisher’s Version Completely derandomized self-adaptation in evolution strategiesPDF
• S. Zimmermann, P. Koumoutsakos, and W. Kinzelbach, “Simulation of Pollutant Transport Using a Particle Method,” J. Comput. Phys. vol. 173, no. 1, pp. 322–347, 2001. Simulation of Pollutant Transport Using a Particle MethodPublisher’s Version Simulation of Pollutant Transport Using a Particle MethodPDF
• T. Werder, J. H. Walther, R. L. Jaffe, T. Halicioglu, F. Noca, and P. Koumoutsakos, “Molecular Dynamics Simulation of Contact Angles of Water Droplets in Carbon Nanotubes,” Nano Lett. vol. 1, no. 12, pp. 697–702, 2001. Molecular Dynamics Simulation of Contact Angles of Water Droplets in Carbon NanotubesPublisher’s Version Molecular Dynamics Simulation of Contact Angles of Water Droplets in Carbon NanotubesPDF
• J. H. Walther and P. Koumoutsakos, “Three-Dimensional Vortex Methods for Particle-Laden Flows with Two-Way Coupling,” J. Comput. Phys. vol. 167, no. 1, pp. 39–71, 2001. Three-Dimensional Vortex Methods for Particle-Laden Flows with Two-Way CouplingPublisher’s Version Three-Dimensional Vortex Methods for Particle-Laden Flows with Two-Way CouplingPDF
• J. H. Walther, R. Jaffe, T. Halicioglu, and P. Koumoutsakos, “Carbon Nanotubes in Water: Structural Characteristics and Energetics,” J. Phys. Chem. B, vol. 105, no. 41, pp. 9980–9987, 2001. Carbon Nanotubes in Water: Structural Characteristics and EnergeticsPublisher’s Version Carbon Nanotubes in Water: Structural Characteristics and EnergeticsPDF
• J. H. Walther and P. Koumoutsakos, “Molecular Dynamics Simulation of Nanodroplet Evaporation,” J. Heat Transf. vol. 123, no. 4, pp. 741, 2001. Molecular Dynamics Simulation of Nanodroplet EvaporationPublisher’s Version Molecular Dynamics Simulation of Nanodroplet EvaporationPDF
• S. D. Mueller, J. H. Walther, and P. D. Koumoutsakos, “Evolution strategies for film cooling optimization,” AIAA J. vol. 39, pp. 537–539, 2001. Evolution strategies for film cooling optimizationPublisher’s Version Evolution strategies for film cooling optimizationPDF
• P. Koumoutsakos, J. Freund, and D. Parekh, “Evolution strategies for automatic optimization of jet mixing,” AIAA J. vol. 39, pp. 967–969, 2001. Evolution strategies for automatic optimization of jet mixingPublisher’s Version Evolution strategies for automatic optimization of jet mixingPDF

2000

• G. Cottet and P. D. Koumoutsakos, “Vortex methods“, Cambridge University Press, 2000, pp. 327. Vortex methods PDF Vortex methods BibTex
• A. Gyr, P. Koumoutsakos, and U. Burr,, “Science and art symposium 2000“, 3rd International Conference on Flow Interaction of Science and Art with Exhibition/Lectures on Interaction of Science & Art, Springer, 2000, pp. 339. Science and art symposium 2000 PDF Science and art symposium 2000 BibTex
• L. Glielmo, M. Milano, and S. Santini, “A machine learning approach to modeling and identification of automotive three-way catalytic converters,” IEEE/ASME Transactions on Mechatronics, vol. 5, no. 2, pp. 132–141, 2000. A machine learning approach to modeling and identification of automotive three-way catalytic convertersPublisher’s Version A machine learning approach to modeling and identification of automotive three-way catalytic convertersPDF
• D. Bueche, N. Sukumar, and B. Moran, “Dispersive properties of the natural element method,” Computational Mechanics, vol. 25, no. 2-3, pp. 207–219, 2000. Dispersive properties of the natural element methodPublisher’s Version Dispersive properties of the natural element methodPDF
• R. Dornberger, D. Bche, P. Stoll, “Multidisciplinary Optimization In Turbomachinery Design”, in European Congress on Computational Methods in Applied Sciences and Engineering ECCOMAS 2000, Barcelona. Multidisciplinary Optimization In Turbomachinery DesignPublisher’s Version Multidisciplinary Optimization In Turbomachinery DesignPDF
• M. Milano, P. Koumoutsakos, “A Clustering Genetic Algorithm for Actuator Optimization in Flow Control”, in Proceedings. The Second NASA/DoD Workshop on Evolvable Hardware. A Clustering Genetic Algorithm for Actuator Optimization in Flow ControlPublisher’s Version A Clustering Genetic Algorithm for Actuator Optimization in Flow ControlPDF
• M. Milano, P. Koumoutsakos, X. Giannakopoulos, J. Schmidhuber, “Evolving strategies for active flow control”, in Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512). Evolving strategies for active flow controlPublisher’s Version Evolving strategies for active flow controlPDF
• S. Muller, I. Sbalzarini, I. Mezic, J. Walther, P. Koumoutsakos, “Evolutionary optimization of mixing microdevices”, in Institute of Computational Sciences, Swiss Federal Institute of Technology Zurich, CH-8092 Zürich, Switzerland. Evolutionary optimization of mixing microdevicesPDF
• S.D. Muller, J. Marchetto, S. Airaghi, P. Kournoutsakos, “Optimization Algorithms based on a Model of Bacterial Chemotaxis”, in IEEE Transactions on Evolutionary Computation, Volume: 6, Issue: 1. Optimization Algorithms based on a Model of Bacterial ChemotaxisPublisher’s Version Optimization Algorithms based on a Model of Bacterial ChemotaxisPDF
• I. F. Sbalzarini, L. K. Su, P. Koumoutsakos, “Evolutionary optimization for flow experiments”, in Center for Turbulence Research Annual Research Briefs 2000. Evolutionary optimization for flow experimentsPDF
• I. F. Sbalzarini†, S. Muller, P. Koumoutsakos, “Multiobjective optimization using evolutionary algorithms”, in Center for Turbulence Research Proceedings of the Summer Program 2000 Multiobjective optimization using evolutionary algorithmsPDF
• J. H. Walther, R. L. Jaffe, T. Halicioglu, P. Koumoutsakos, “Molecular dynamics simulations of carbon nanotubes in water”, in Center for Turbulence Research Proceedings of the Summer Program 2000. Molecular dynamics simulations of carbon nanotubes in waterPublisher’s Version Molecular dynamics simulations of carbon nanotubes in waterPDF
• G. H. Cottet, I. F. Sbalzarini, S. Muller, P. Koumoutsakos, “Optimization of trailing vortex destruction by evolution strategies”, in Center for Turbulence Research Proceedings of the Summer Program 2000. Optimization of trailing vortex destruction by evolution strategiesPublisher’s Version Optimization of trailing vortex destruction by evolution strategiesPDF
• R. Dornberger, P. Stoll, D. Bueche, A. Neu, “Multidisciplinary turbomachinery blade design optimization”, in 38th Aerospace Sciences Meeting and Exhibit. Multidisciplinary turbomachinery blade design optimizationPublisher’s Version Multidisciplinary turbomachinery blade design optimizationPDF

1999

• P. Marino, M. Milano, and F. Vasca, “Linear quadratic state feedback and robust neural network estimator for field-oriented-controlled induction motors,” IEEE Transactions on Industrial Electronics, vol. 46, no. 1, pp. 150–161, 1999. Linear quadratic state feedback and robust neural network estimator for field-oriented-controlled induction motorsPublisher’s Version Linear quadratic state feedback and robust neural network estimator for field-oriented-controlled induction motorsPDF
• P. Koumoutsakos, P. Moin, “Algorithms for Shear Flow Control and Optimization”, in Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304). Algorithms for Shear Flow Control and OptimizationPublisher’s Version Algorithms for Shear Flow Control and OptimizationPDF
• S. Muller, M. Milano, P. Koumoutsakos, “Application of machine learning algorithms to flow modeling and optimization”, in Center for Turbulence Research Annual Research Briefs 1999. Application of machine learning algorithms to flow modeling and optimizationPublisher’s Version Application of machine learning algorithms to flow modeling and optimizationPDF

1998

• A. Larsen and J. H. Walther, “Discrete vortex simulation of flow around five generic bridge deck sections,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 77-78, pp. 591–602, 1998. Discrete vortex simulation of flow around five generic bridge deck sectionsPublisher’s Version Discrete vortex simulation of flow around five generic bridge deck sectionsPDF
• P. Koumoutsakos, “Particle methods for micro and macroscale flow simulations”, in Center for Turbulence Research Annual Research Briefs 1998. Particle methods for micro and macroscale flow simulationsPDF
• P. Koumoutsakos, J. Freund, D. Parekh, “Evolution strategies for parameter optimization in jet flow control”, in Center for Turbulence Research Proceedings of the Summer Program 1998. Evolution strategies for parameter optimization in jet flow controlPublisher’s Version Evolution strategies for parameter optimization in jet flow controlPDF
• J. H. Walther, A. Larsen, “A Two Dimensional Discrete Vortex Method for Bridge Aerodynamics Applications”, in Journal of Wind Engineering and Industrial Aerodynamics, Volumes 67–68, April–June 1997, Pages 183-193. A Two Dimensional Discrete Vortex Method for Bridge Aerodynamics ApplicationsPublisher’s Version A Two Dimensional Discrete Vortex Method for Bridge Aerodynamics ApplicationsPDF
• J. H. Walther, “Discrete vortex methods in bridge aerodynamics and prospects for parallel computing techniques”, in International Symposium on Advances in Bridge Aerodynamics, 1998. Discrete vortex methods in bridge aerodynamics and prospects for parallel computing techniquesPDF

1997

• L. Milano, F. Barone, and M. Milano, “Time domain amplitude and frequency detection of gravitational waves from coalescing binaries,” Physical Review D, vol. 55, no. 8, pp. 4537–4554, 1997. Time domain amplitude and frequency detection of gravitational waves from coalescing binariesPublisher’s Version Time domain amplitude and frequency detection of gravitational waves from coalescing binariesPDF
• L. Milano, F. Barone, and M. Milano, “Detection of gravitational waves from coalescing binaries in the time domain,” Classical and Quantum Gravity, vol. 14, no. 6, pp. 1531–1536, 1997. Detection of gravitational waves from coalescing binaries in the time domainPublisher’s Version Detection of gravitational waves from coalescing binaries in the time domainPDF
• J. H. Walther and A. Larsen, “Two dimensional discrete vortex method for application to bluff body aerodynamics,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 67-68, pp. 183–193, 1997. Two dimensional discrete vortex method for application to bluff body aerodynamicsPublisher’s Version Two dimensional discrete vortex method for application to bluff body aerodynamicsPDF
• A. Larsen and J. H. Walther, “Aeroelastic analysis of bridge girder sections based on discrete vortex simulations,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 66-67, pp. 253–265, 1997. Aeroelastic analysis of bridge girder sections based on discrete vortex simulationsPublisher’s Version Aeroelastic analysis of bridge girder sections based on discrete vortex simulationsPDF
• J. H. Walther, A. Larsen, “Analytical and Discrete Vortex Models for an Oscillating Flat Plate with Trailing Edge Flap”, in Preprint submitted to Elsevier Preprint. Analytical and Discrete Vortex Models for an Oscillating Flat Plate with Trailing Edge FlapPDF

1993

• A. Leonard, P. Koumoutsakos, “High Resolution Vortex Simulation of Bluff Body Flows”, in Journal of Wind Engineering and Industrial Aerodynamics, Volumes 46–47, August 1993, Pages 315-325. High Resolution Vortex Simulation of Bluff Body FlowsPublisher’s Version High Resolution Vortex Simulation of Bluff Body FlowsPDF