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Modeling physical processes with deep learning : a dynamical systems approach

Abstract : Deep Learning has emerged as a predominant tool for AI, and has already abundant 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 developped. 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 one of modelling complex physical processes, leveraging deep learning methods in order to make up for lacking prior knowledge. The second objective is somewhat its converse: 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.
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Submitted on : Tuesday, January 11, 2022 - 3:08:14 PM
Last modification on : Saturday, July 9, 2022 - 3:27:54 AM

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  • HAL Id : tel-03521354, version 2

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Emmanuel de Bézenac. Modeling physical processes with deep learning : a dynamical systems approach. Artificial Intelligence [cs.AI]. Sorbonne Université, 2021. English. ⟨NNT : 2021SORUS203⟩. ⟨tel-03521354v2⟩

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