Search Results for author: Bertrand Chapron

Found 11 papers, 4 papers with code

Online Calibration of Deep Learning Sub-Models for Hybrid Numerical Modeling Systems

no code implementations17 Nov 2023 Said Ouala, Bertrand Chapron, Fabrice Collard, Lucile Gaultier, Ronan Fablet

Most of these efforts in defining hybrid dynamical systems follow {offline} learning strategies in which the neural parameterization (called here sub-model) is trained to output an ideal correction.

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.

Guided Unsupervised Learning by Subaperture Decomposition for Ocean SAR Image Retrieval

no code implementations29 Sep 2022 Nicolae-Cătălin Ristea, Andrei Anghel, Mihai Datcu, Bertrand Chapron

Spaceborne synthetic aperture radar (SAR) can provide accurate images of the ocean surface roughness day-or-night in nearly all weather conditions, being an unique asset for many geophysical applications.

Image Retrieval Retrieval

Multimodal 4DVarNets for the reconstruction of sea surface dynamics from SST-SSH synergies

1 code implementation4 Jul 2022 Ronan Fablet, Quentin Febvre, Bertrand Chapron

We introduce a trainable multimodal inversion scheme for the reconstruction of sea surface dynamics from multi-source satellite-derived observations.

Guided deep learning by subaperture decomposition: ocean patterns from SAR imagery

no code implementations9 Apr 2022 Nicolae-Catalin Ristea, Andrei Anghel, Mihai Datcu, Bertrand Chapron

Overall, we encourage the development of data centring approaches, showing that, data preprocessing could bring significant performance improvements over existing deep learning models.

Multimodal learning-based inversion models for the space-time reconstruction of satellite-derived geophysical fields

1 code implementation20 Mar 2022 Ronan Fablet, Bertrand Chapron

For numerous earth observation applications, one may benefit from various satellite sensors to address the reconstruction of some process or information of interest.

Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning

no code implementations11 Feb 2022 Said Ouala, Steven L. Brunton, Ananda Pascual, Bertrand Chapron, Fabrice Collard, Lucile Gaultier, Ronan Fablet

The complexity of real-world geophysical systems is often compounded by the fact that the observed measurements depend on hidden variables.

Learning Runge-Kutta Integration Schemes for ODE Simulation and Identification

no code implementations11 May 2021 Said Ouala, Laurent Debreu, Ananda Pascual, Bertrand Chapron, Fabrice Collard, Lucile Gaultier, Ronan Fablet

Inevitably, a numerical simulation of a differential equation will then always be distinct from a true analytical solution.

The speed of breaking waves controls sea surface drag

no code implementations11 Mar 2021 Alex Ayet, Bertrand Chapron, Peter Sutherland, Gabriel G. Katul

Originally suggested on purely dimensional grounds, this roughness length does not directly correspond to a measurable physical quantity of the wind-and-wave system.

Fluid Dynamics Atmospheric and Oceanic Physics

Learning Variational Data Assimilation Models and Solvers

2 code implementations25 Jul 2020 Ronan Fablet, Bertrand Chapron, Lucas. Drumetz, Etienne Memin, Olivier Pannekoucke, Francois Rousseau

Intriguingly, we also show that the variational models issued from the true Lorenz-63 and Lorenz-96 ODE representations may not lead to the best reconstruction performance.

Learning Latent Dynamics for Partially-Observed Chaotic Systems

1 code implementation4 Jul 2019 Said Ouala, Duong Nguyen, Lucas. Drumetz, Bertrand Chapron, Ananda Pascual, Fabrice Collard, Lucile Gaultier, Ronan Fablet

This paper addresses the data-driven identification of latent dynamical representations of partially-observed systems, i. e., dynamical systems for which some components are never observed, with an emphasis on forecasting applications, including long-term asymptotic patterns.

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