61 citations · 104 across the 6 of their papers we have counts for
6 papers
Towards fully differentiable neural ocean model with Veros
Etienne Meunier, Said Ouala, Hugo Frezat +2
We present a differentiable extension of the VEROS ocean model, enabling automatic differentiation through its dynamical core. We describe the key modifications required to make th…
Online learning of subgrid-scale models for quasi-geostrophic turbulence in planetary interiors
Hugo Frezat, Thomas Gastine, Alexandre Fournier
Machine learning approaches to subgrid-scale (SGS) modelling are now well established in atmospheric and oceanic applications. Among these, online end-to-end learning, where the di…
Adjoint-based online learning of two-layer quasi-geostrophic baroclinic turbulence
Fei Er Yan, Hugo Frezat, Julien Le Sommer +2
For reasons of computational constraint, most global ocean circulation models used for Earth System Modeling still rely on parameterizations of sub-grid processes, and limitations…
Gradient-free online learning of subgrid-scale dynamics with neural emulators
Hugo Frezat, Ronan Fablet, Guillaume Balarac +1
In this paper, we propose a generic algorithm to train machine learning-based subgrid parametrizations online, i.e., with loss functions, but for non-differ…
A posteriori learning for quasi-geostrophic turbulence parametrization
Hugo Frezat, Julien Le Sommer, Ronan Fablet +2
The use of machine learning to build subgrid parametrizations for climate models is receiving growing attention. State-of-the-art strategies address the problem as a supervised lea…
Physical invariance in neural networks for subgrid-scale scalar flux modeling
Hugo Frezat, Guillaume Balarac, Julien Le Sommer +2
In this paper we present a new strategy to model the subgrid-scale scalar flux in a three-dimensional turbulent incompressible flow using physics-informed neural networks (NNs). Wh…