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

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Herbert S. Winokur Jr. Professor of Computing in Science and Engineering

323 Pierce Hall, 29 Oxford Street Cambridge, MA 02138

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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