188 citations · 539 across the 17 of their papers we have counts for
4 papers · 1 filter
Multiscale Neural PDE Surrogates for Prediction and Downscaling: Application to Ocean Currents
Abdessamad El-Kabid, Loubna Benabbou, Redouane Lguensat +1
Accurate modeling of physical systems governed by partial differential equations is a central challenge in scientific computing. In oceanography, high-resolution current data are c…
Learning to generate physical ocean states: Towards hybrid climate modeling
Etienne Meunier, David Kamm, Guillaume Gachon +2
Ocean General Circulation Models require extensive computational resources to reach equilibrium states, while deep learning emulators, despite offering fast predictions, lack the p…
A posteriori learning of quasi-geostrophic turbulence parametrization: an experiment on integration steps
Hugo Frezat, Julien Le Sommer, Ronan Fablet +2
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 sim…
Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Models
Redouane Lguensat, Julien Le Sommer, Sammy Metref +2
We introduce a new strategy designed to help physicists discover hidden laws governing dynamical systems. We propose to use machine learning automatic differentiation libraries to…