Séminaire
Nom intervenant
Nathan Doumèche
Résumé
Physics-informed neural networks (PINNs) combine the expressiveness of neural networks with the interpretability of physical modeling. Their good practical performance has been demonstrated both in the context of solving partial differential equations and in the context of hybrid modeling, which consists of combining an imperfect physical model with noisy observations. However, most of their theoretical properties remain to be established. We offer some statistical guidelines into the proper use of PINNs.
Lieu
Amphi C2.0.037
Date du jour
Date de fin du Workshop