MIAI Chair R-GAINS 2025 - 2029 (co-PI)

Robust Generative Artificial Intelligence for physical Sciences: From Monte Carlo sampling to multiscale dynamics generation #

Team: Vivien LECOMTE (LIPhy, UGA, co-PI), Manon MICHEL (LMBP, UCA, co-PI), Alain DEQUIDT (ICCF, UCA), Arnaud GUILLIN (LMBP, UCA), Misaki OZAWA (LIPhy, UGA)

This project is a joint collaboration between the Université Clermont Auvergne (UCA) and the Université Grenoble Alpes (UGA). The inte with strong emphasis on mathematical guarantees and educational initiatirdisciplinary team includes physicists, chemists, and mathematicians working together to address data generation challenges, which is one of the central issues in scientific computing. In particular, we aim to develop efficient Monte Carlo sampling methods enhanced by state-of-the-art generative models and providing mathematical guarantees regarding the robustness and reliability of the developed machine learning approaches. We will also design advanced rare event sampling techniques by integrating modern stochastic process theory with machine learning. Additionally, we focus on predicting dynamics from molecular simulation data. Beyond research, we plan to organize various educational initiatives, including new lecture courses on machine learning, hackathons, CNRS professional training sessions, and international summer schools, in order to promote both scientific research and training across MIAI network.

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