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20192023
most citedA posteriori learning for quasi-geostrophic turbulence parametrization

61 citations · 133 across the 11 of their papers we have counts for

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5 papers · 1 filter

cs.LG2023★ 1 cited

OceanBench: The Sea Surface Height Edition

J. Emmanuel Johnson, Quentin Febvre, Anastasia Gorbunova +4

The ocean profoundly influences human activities and plays a critical role in climate regulation. Our understanding has improved over the last decades with the advent of satellite…

cs.LG2023

Scale-aware neural calibration for wide swath altimetry observations

Quentin Febvre, Clément Ubelmann, Julien Le Sommer +1

Sea surface height (SSH) is a key geophysical parameter for monitoring and studying meso-scale surface ocean dynamics. For several decades, the mapping of SSH products at regional…

cs.LG2021

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…

cs.LG2021★ 15 cited

Joint calibration and mapping of satellite altimetry data using trainable variational models

Quentin Febvre, Ronan Fablet, Julien Le Sommer +1

Satellite radar altimeters are a key source of observation of ocean surface dynamics. However, current sensor technology and mapping techniques do not yet allow to systematically r…

cs.LG2019★ 10 cited

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…