Deep learning based physical-statistics modeling of ocean dynamics

• Main Author: Déchelle-Marquet, Marie
• Other Authors: Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université, Patrick Gallinari, Marina Lévy
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: HAL CCSD 2023
• Subjects: Neural networks; Physics-guided; Hybrid modeling; Apprentissage profond; Réseaux de neurones; Modélisation physique; Modèles hybrides; Données océanographiques; Données réelles; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]; [PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph]; North Atlantic
• Online Access: https://theses.hal.science/tel-04166816; https://theses.hal.science/tel-04166816/document; https://theses.hal.science/tel-04166816/file/DECHELLE_MARQUET_Marie_these_2023.pdf

The modeling of dynamical phenomena in geophysics and climate is based on a deep understanding of the underlying physics, described in the form of PDEs, and on their resolution by numerical models. The ever-increasing number of observations of physical systems, the recent rise of deep learning and the huge computational power required by numerical solvers, which hinders the resolution of existing models, suggest that the future of physical models could be data-driven. But for this prognosis to come true, deep learning must tackle several challenges, such as the interpretability and physical consistency of deep models, still largely under-addressed by the deep learning community.In this thesis, we address both challenges: we study the prediction of sea surface temperature (SST) using hybrid models combining a data-driven and a physical model. Ensuring the physical plausibility of hybrid models necessitates well-posing their learning: otherwise, the high versatility of neural networks may lead the data-driven part to bypass the physical part.Our study is divided into two parts: a theoretical study on hybrid models, and a practical confrontation of our model on simulations of real data. First, we propose a new generic well- posed learning framework based on the optimization of an upper-bound of a prediction error. Second, we study real-like ocean observations of SST and velocity fields from the Gulf Stream current in the North Atlantic (from the NATL60 model). This application highlights the challenges raised by confronting physics aware learning to the complexity of real-world physics. It also raises issues such as model generalization, which we discuss as a possible perspective. La modélisation des phénomènes dynamiques en géophysique repose sur une compréhension de la physique sous-jacente, décrite sous la forme d'EDP, et sur leur résolution par des modèles numériques. Le nombre croissant d'observations de systèmes physiques, l'essor récent de l'apprentissage profond et l'énorme puissance de calcul requise par ...