Our paper “Scientific multi-agent reinforcement learning for wall-models of turbulent flows” has been featured as a Nature Communications Editors’ Highlight

Recent Publications

• 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

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