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