Search Results for author: Julien Le Sommer

Found 12 papers, 6 papers with code

Gradient-free online learning of subgrid-scale dynamics with neural emulators

no code implementations30 Oct 2023 Hugo Frezat, Ronan Fablet, Guillaume Balarac, Julien Le Sommer

It is demonstrated that training the neural emulator and parametrization components separately with different loss quantities is necessary in order to minimize the propagation of approximation biases.

Training neural mapping schemes for satellite altimetry with simulation data

no code implementations19 Sep 2023 Quentin Febvre, Julien Le Sommer, Clément Ubelmann, Ronan Fablet

Here, we leverage both simulations of ocean dynamics and satellite altimeters to train simulation-based neural mapping schemes for the sea surface height and demonstrate their performance for real altimetry datasets.


Scale-aware neural calibration for wide swath altimetry observations

no code implementations9 Feb 2023 Quentin Febvre, Clément Ubelmann, Julien Le Sommer, Ronan Fablet

Sea surface height (SSH) is a key geophysical parameter for monitoring and studying meso-scale surface ocean dynamics.

Inversion of sea surface currents from satellite-derived SST-SSH synergies with 4DVarNets

no code implementations23 Nov 2022 Ronan Fablet, Bertrand Chapron, Julien Le Sommer, Florian Sévellec

This is however limited to the surface-constrained geostrophic component of sea surface velocities.

Neural Fields for Fast and Scalable Interpolation of Geophysical Ocean Variables

1 code implementation18 Nov 2022 J. Emmanuel Johnson, Redouane Lguensat, Ronan Fablet, Emmanuel Cosme, Julien Le Sommer

Optimal Interpolation (OI) is a widely used, highly trusted algorithm for interpolation and reconstruction problems in geosciences.

A DNN Framework for Learning Lagrangian Drift With Uncertainty

1 code implementation12 Apr 2022 Joseph Jenkins, Adeline Paiement, Yann Ourmières, Julien Le Sommer, Jacques Verron, Clément Ubelmann, Hervé Glotin

Reconstructions of Lagrangian drift, for example for objects lost at sea, are often uncertain due to unresolved physical phenomena within the data.

A posteriori learning for quasi-geostrophic turbulence parametrization

no code implementations8 Apr 2022 Hugo Frezat, Julien Le Sommer, Ronan Fablet, Guillaume Balarac, Redouane Lguensat

State-of-the-art strategies address the problem as a supervised learning task and optimize algorithms that predict subgrid fluxes based on information from coarse resolution models.

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

1 code implementation12 Nov 2021 Hugo Frezat, Julien Le Sommer, Ronan Fablet, Guillaume Balarac, Redouane Lguensat

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.

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

no code implementations7 Oct 2021 Quentin Febvre, Ronan Fablet, Julien Le Sommer, Clément Ubelmann

The proposed framework significantly outperforms the operational state-of-the-art mapping pipeline and truly benefits from wide-swath data to resolve finer scales on the global map as well as in the SWOT sensor geometry.

Physical invariance in neural networks for subgrid-scale scalar flux modeling

1 code implementation9 Oct 2020 Hugo Frezat, Guillaume Balarac, Julien Le Sommer, Ronan Fablet, Redouane Lguensat

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).

Filtering Internal Tides From Wide-Swath Altimeter Data Using Convolutional Neural Networks

1 code implementation3 May 2020 Redouane Lguensat, Ronan Fablet, Julien Le Sommer, Sammy Metref, Emmanuel Cosme, Kaouther Ouenniche, Lucas. Drumetz, Jonathan Gula

The upcoming Surface Water Ocean Topography (SWOT) satellite altimetry mission is expected to yield two-dimensional high-resolution measurements of Sea Surface Height (SSH), thus allowing for a better characterization of the mesoscale and submesoscale eddy field.

Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Models

1 code implementation20 Nov 2019 Redouane Lguensat, Julien Le Sommer, Sammy Metref, Emmanuel Cosme, Ronan Fablet

We introduce a new strategy designed to help physicists discover hidden laws governing dynamical systems.

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