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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.
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Preprints, Working Papers, ...
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https://hal.archives-ouvertes.fr/hal-03456259
Contributor : Hugo Frezat Connect in order to contact the contributor
Submitted on : Tuesday, January 11, 2022 - 5:31:51 PM
Last modification on : Thursday, April 7, 2022 - 1:58:15 PM

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  • HAL Id : hal-03456259, version 2
  • ARXIV : 2111.06841

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Hugo Frezat, Julien Le Sommer, Ronan Fablet, Guillaume Balarac, Redouane Lguensat. A posteriori learning of quasi-geostrophic turbulence parametrization: an experiment on integration steps. 2022. ⟨hal-03456259v2⟩

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