Skip to Main content Skip to Navigation
New interface

Modeling Physical Processes with Deep Learning: A Dynamical Systems Approach

Abstract : Deep Learning has emerged as a predominant tool for AI, and has already many applications in fields where data is abundant and access to prior knowledge is difficult. This is not necessarily the case for natural sciences, and in particular, for physical processes. Indeed, these have been the object of study since centuries, a vast amount of knowledge has been acquired, and elaborate algorithms and methods have been developed. Thus, this thesis has two main objectives. The first considers the study of the role that deep learning has to play in this vast ecosystem of knowledge, theory and tools. We will attempt to answer this general question through a concrete problem: the modelling complex physical processes, leveraging deep learning methods in order to make up for lacking prior knowledge. The second objective is somewhat its dual: it focuses on how perspectives, insights and tools from the field of study of physical processes and dynamical systems can be applied in the context of deep learning, in order to gain a better understanding and develop novel algorithms.
Complete list of metadata
Contributor : Emmanuel de Bézenac Connect in order to contact the contributor
Submitted on : Thursday, December 16, 2021 - 6:45:36 PM
Last modification on : Wednesday, January 19, 2022 - 2:08:02 PM
Long-term archiving on: : Thursday, March 17, 2022 - 7:59:37 PM


Files produced by the author(s)


  • HAL Id : tel-03521354, version 1


Emmanuel de Bézenac. Modeling Physical Processes with Deep Learning: A Dynamical Systems Approach. Machine Learning [cs.LG]. Sorbonne Université, 2021. English. ⟨NNT : ⟩. ⟨tel-03521354v1⟩



Record views


Files downloads