Search Results for author: Redouane Lguensat

Found 8 papers, 6 papers with code

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.

Bridging observation, theory and numerical simulation of the ocean using Machine Learning

no code implementations26 Apr 2021 Maike Sonnewald, Redouane Lguensat, Daniel C. Jones, Peter D. Dueben, Julien Brajard, Venkatramani Balaji

Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study.

Time Series

NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations

1 code implementation13 Nov 2020 Paula Harder, William Jones, Redouane Lguensat, Shahine Bouabid, James Fulton, Dánell Quesada-Chacón, Aris Marcolongo, Sofija Stefanović, Yuhan Rao, Peter Manshausen, Duncan Watson-Parris

The recent explosion in applications of machine learning to satellite imagery often rely on visible images and therefore suffer from a lack of data during the night.


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.

EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies

1 code implementation10 Nov 2017 Redouane Lguensat, Miao Sun, Ronan Fablet, Evan Mason, Pierre Tandeo, Ge Chen

This work presents EddyNet, a deep learning based architecture for automated eddy detection and classification from Sea Surface Height (SSH) maps provided by the Copernicus Marine and Environment Monitoring Service (CMEMS).

General Classification Oceanic Eddy Classification

Reinforcement Learning Based Algorithm for the Maximization of EV Charging Station Revenue

no code implementations4 Jul 2014 Stoyan Dimitrov, Redouane Lguensat

This paper presents an online reinforcement learning based application which increases the revenue of one particular electric vehicles (EV) station, connected to a renewable source of energy.


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