Fiche de YIN Yuan

Titre : Physics-Aware Deep Learning: Hybrid Modeling and Generalization
Date : 28 juin 2023
Heure: 14:00
Résumé : Deep learning has made significant progress in various fields and has emerged as a promising tool for modeling physical dynamical phenomena that exhibit highly nonlinear relationships. However, existing approaches are limited in their ability to make physically sound predictions due to the lack of prior knowledge and to handle real-world scenarios where data comes from multiple dynamics or is irregularly distributed in time and space. This thesis aims to overcome these limitations in the following directions: improving neural network-based dynamics modeling by leveraging physical models through hybrid modeling; extending the generalization power of dynamics models by learning commonalities from data of different dynamics to extrapolate to unseen systems; and handling free-form data and continuously predicting phenomena in time and space. The proposed directions show promise for improving their accaccuracy and generalization power, paving the way for future research in new applications.
Lieu : Salle 304, barre 65-66 4 place Jussieu Campus Pierre et Marie Curie Sorbonne Université 75005 Paris