Methodological and theoretical advances in Physics-Informed Machine Learning>

Planning

Wednesday, April 15, 2026

Time Event (+)
09:00 - 09:30 Welcome  
09:30 - 10:20 Reinforcement learning (Amphi A0.04 ) (+)  
09:30 - 10:20 › Moving from optimal control to continuous-time reinforcement learning using PINNs - Alena Shilova, Centre Inria de Saclay  
10:20 - 10:50 Coffee break (Sortie A0.04)  
10:50 - 11:40 Optimisation (Amphi A0.04 ) (+)  
10:50 - 11:40 › Bridging Physics and Learning: An Optimization Framework for Hybrid Differentiable Models - Jordan Patracone, Université Jean Monnet - Saint-Étienne  
11:40 - 12:30 Numerical methods (Amphi A0.04 )  
12:30 - 14:00 Lunch (Canopée)  
14:00 - 14:50 Generative models (Amphi A0.04 ) (+)  
14:00 - 14:50 › Propagating Uncertainty in Stochastic Differential Equations with Neural Solvers of the Fokker Planck Equation - Lucas Drumetz, IMT Atlantique  
14:50 - 15:40 Optimal control (Amphi A0.04 ) (+)  
14:50 - 15:40 › An optimal control problem on a data-driven reduced model in epidemiology - Clémentine Courtès, Institut de Recherche Mathématique Avancée  
15:40 - 16:10 Coffee break (Sortie A0.04)  
16:10 - 17:00 Inverse problems (Amphi A0.04 ) (+)  
16:10 - 17:00 › An optimal control problem on a data-driven reduced model in epidemiology - Arnaud Vadeboncoeur, Cambridge University  
17:00 - 18:30 Poster Session (Sortie A0.04) - Poster Session  
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