Welcome to Philip Nielsen! September 2, 2024 Philip Nielsen is visiting us and will apply his knowledge in Reinforcement Learning and Generative models to the solving Partial Differential equations….Continue Reading Welcome to Philip Nielsen!
Welcome to Abhinav Singh! September 1, 2024 We’re happy to have Abhinav joining our team as a Postdoc. Abhinav will be working on Reinforcement Learning for active matter and will be studying the development of Neurons coupled with artificial electric inputs. …Continue Reading Welcome to Abhinav Singh!
Our paper “Vortex separation cascades in simulations of the planar flow past an impulsively started cylinder up to Re=100 000” was published in the Journal of Fluid Mechanics Our paper “Vortex separation cascades in simulations of the planar flow past an impulsively started cylinder up to Re=100 000” was published in the Journal of Fluid Mechanics…Continue Reading Our paper “Vortex separation cascades in simulations of the planar flow past an impulsively started cylinder up to Re=100 000” was published in the Journal of Fluid Mechanics
Our viewpoint “Bayesian uncertainty quantification for machine-learned models in physics” was published in Nature Reviews Physics Our paper “Bayesian uncertainty quantification for machine-learned models in physics” was published in Nature Reviews Physics…Continue Reading Our viewpoint “Bayesian uncertainty quantification for machine-learned models in physics” was published in Nature Reviews Physics
Our paper “Scientific multi-agent reinforcement learning for wall-models of turbulent flows” has been featured as a Nature Communications Editors’ Highlight The editors at Nature Communications have selected our paper “Scientific multi-agent reinforcement learning for wall-models of turbulent flows” to be featured on the Editors’ Highlights webpage….Continue Reading Our paper “Scientific multi-agent reinforcement learning for wall-models of turbulent flows” has been featured as a Nature Communications Editors’ Highlight
Bubbles, bubbles everywhere Novel method simulates tens of thousands of bubbles in foamy flows February 2, 2024 Our research has recently been featured on the Harvard SEAS website….Continue Reading Bubbles, bubbles everywhere Novel method simulates tens of thousands of bubbles in foamy flows
Our paper “Nanopumps without pressure gradients: ultrafast transport of water in patterned nanotubes” was published and featured on the cover of Journal of Physical Chemistry B Our paper “Nanopumps without pressure gradients: ultrafast transport of water in patterned nanotubes” was published and featured on the cover of Journal of Physical Chemistry B…Continue Reading Our paper “Nanopumps without pressure gradients: ultrafast transport of water in patterned nanotubes” was published and featured on the cover of Journal of Physical Chemistry B
Our paper “Independent control and path planning of microswimmers with a uniform magnetic field” was published in Advanced Intelligent Systems December 27, 2021 Our paper “Independent control and path planning of microswimmers with a uniform magnetic field” was published in Advanced Intelligent Systems….Continue Reading Our paper “Independent control and path planning of microswimmers with a uniform magnetic field” was published in Advanced Intelligent Systems
Our paper “Learning Efficient Navigation in Vortical Flow Fields” was published in Nature Communications. December 9, 2021 Our paper “Learning Efficient Navigation in Vortical Flow Fields” was published in Nature Communications….Continue Reading Our paper “Learning Efficient Navigation in Vortical Flow Fields” was published in Nature Communications.
Our paper “Efficient and Scalable Software Framework for Bayesian Uncertainty Quantification and Stochastic Optimization” has been published in Computer Methods in Applied Mechanics and Engineering November 29, 2021 Our paper “Efficient and Scalable Software Framework for Bayesian Uncertainty Quantification and Stochastic Optimization” has been published in Computer Methods in Applied Mechanics and Engineering….Continue Reading Our paper “Efficient and Scalable Software Framework for Bayesian Uncertainty Quantification and Stochastic Optimization” has been published in Computer Methods in Applied Mechanics and Engineering
