Gradient-free online learning of subgrid-scale dynamics with neural emulators - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2023

Gradient-free online learning of subgrid-scale dynamics with neural emulators

Résumé

In this paper, we propose a generic algorithm to train machine learning-based subgrid parametrizations online, i.e., with $\textit{a posteriori}$ loss functions for non-differentiable numerical solvers. The proposed approach leverage neural emulators to train an approximation of the reduced state-space solver, which is then used to allows gradient propagation through temporal integration steps. The algorithm is able to recover most of the benefit of online strategies without having to compute the gradient of the original solver. It is demonstrated that training the neural emulator and parametrization components separately with respective loss quantities is necessary in order to minimize the propagation of some approximation bias.

Dates et versions

hal-04312283 , version 1 (28-11-2023)

Licence

Paternité

Identifiants

Citer

Hugo Frezat, Ronan Fablet, Guillaume Balarac, Julien Le Sommer. Gradient-free online learning of subgrid-scale dynamics with neural emulators. 2023. ⟨hal-04312283⟩
11 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More