Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

A posteriori learning of quasi-geostrophic turbulence parametrization: an experiment on integration steps

Abstract : Modeling the subgrid-scale dynamics of reduced models is a long standing open problem that finds application in ocean, atmosphere and climate predictions where direct numerical simulation (DNS) is impossible. While neural networks (NNs) have already been applied to a range of three-dimensional problems with success, the backward energy transfer of two-dimensional flows still remains a stability issue for trained models. We show that learning a model jointly with the dynamical solver and a meaningful $\textit{a posteriori}$-based loss function lead to stable and realistic simulations when applied to quasi-geostrophic turbulence.
Document type :
Preprints, Working Papers, ...
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03456259
Contributor : Julien Le Sommer Connect in order to contact the contributor
Submitted on : Monday, November 29, 2021 - 11:31:52 PM
Last modification on : Thursday, January 13, 2022 - 5:01:25 AM

File

2111.06841.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03456259, version 1
  • ARXIV : 2111.06841

Collections

Citation

Hugo Frezat, Julien Le Sommer, Ronan Fablet, Guillaume Balarac, Redouane Lguensat. A posteriori learning of quasi-geostrophic turbulence parametrization: an experiment on integration steps. 2021. ⟨hal-03456259v1⟩

Share

Metrics

Les métriques sont temporairement indisponibles