Description
Date depot: 20 mai 2022
Titre: INFORMED DEEP LEARNING for MODELING PHYSICAL DYNAMICS
Directeur de thèse:
Abdenour HADID (ISIR (EDITE))
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Intelligence artificielle
Resumé: Deep learning offers a new data driven approaches to the modeling of dynamical systems underlying natural observations. This has recently given rise to new and prolific research topics focused on exploiting deep learning methods for modeling spatio-temporal dynamics. The use of deep learning and data-driven approaches for modeling natural phenomena from physical observations however suffers from limitations such as the lack of generalization, robustness and physical plausibility. Modeling natural phenomena from physical observations indeed raises new challenges for machine learning and deep learning. Being the result of multiple interacting physical processes, the observed phenomena can be extremely complex. The data are heterogeneous, noisy, and even when plentiful they usually represent scarce and partial information about the underlying process. So, the main question which arises is: how to build accurate and fast machine learning models from this limited training data. Instead of fully relying on data for capturing the underlying complex phenomena, it would be appealing to incorporate physical knowledge and priors into data driven deep learning systems. A related challenge is to develop physically meaningful statistical models.
Doctorant.e: Elabid Zakaria