Augmenting physical models with deep networks for complex dynamics forecasting - Archive ouverte HAL Access content directly
Journal Articles Journal of Statistical Mechanics: Theory and Experiment Year : 2021

Augmenting physical models with deep networks for complex dynamics forecasting

Abstract

Forecasting complex dynamical phenomena in settings where only partial knowledge of their dynamics is available is a prevalent problem across various scientific fields. While purely data-driven approaches are arguably insufficient in this context, standard physical modeling based approaches tend to be over-simplistic, inducing nonnegligible errors. In this work, we introduce the APHYNITY framework, a principled approach for augmenting incomplete physical dynamics described by differential equations with deep data-driven models. It consists in decomposing the dynamics into two components: a physical component accounting for the dynamics for which we have some prior knowledge, and a data-driven component accounting for errors of the physical model. The learning problem is carefully formulated such that the physical model explains as much of the data as possible, while the data-driven component only describes information that cannot be captured by the physical model, no more, no less. This not only provides the existence and uniqueness for this decomposition, but also ensures interpretability and benefits generalization. Experiments made on three important use cases, each representative of a different family of phenomena, i.e. reactiondiffusion equations, wave equations and the non-linear damped pendulum, show that APHYNITY can efficiently leverage approximate physical models to accurately forecast the evolution of the system and correctly identify relevant physical parameters. Code is available at https://github.com/yuan-yin/APHYNITY.
Fichier principal
Vignette du fichier
APHYNITY-JSTAT.pdf (1.04 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03508401 , version 1 (12-01-2022)
hal-03508401 , version 2 (10-05-2022)

Identifiers

Cite

Yuan Yin, Vincent Le Guen, Jérémie Donà, Emmanuel de Bézenac, Ibrahim Ayed, et al.. Augmenting physical models with deep networks for complex dynamics forecasting. Journal of Statistical Mechanics: Theory and Experiment, 2021, 2021 (12), pp.124012. ⟨10.1088/1742-5468/ac3ae5⟩. ⟨hal-03508401v2⟩
179 View
90 Download

Altmetric

Share

Gmail Facebook X LinkedIn More