Search Results for author: Redouane Lguensat

Found 13 papers, 9 papers with code

Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning

no code implementations21 Oct 2023 William Yik, Maike Sonnewald, Mariana C. A. Clare, Redouane Lguensat

To understand changes in the Antarctic Circumpolar Current, we extend the method Tracking global Heating with Ocean Regimes (THOR) to a mesoscale eddy permitting climate model and identify regions of the ocean characterized by similar physics, called dynamical regimes, using readily accessible fields from climate models.

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.

Semi-automatic tuning of coupled climate models with multiple intrinsic timescales: lessons learned from the Lorenz96 model

1 code implementation11 Aug 2022 Redouane Lguensat, Julie Deshayes, Homer Durand, V. Balaji

The objective of this study is to evaluate the potential for History Matching (HM) to tune a climate system with multi-scale dynamics.


Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamics

1 code implementation30 Apr 2022 Mariana C. A. Clare, Maike Sonnewald, Redouane Lguensat, Julie Deshayes, Venkatramani Balaji

The uncertainty analysis from the BNN provides a comprehensive overview of the prediction more suited to practitioners' needs than predictions from a classical neural network.

Decision Making Explainable artificial intelligence +1

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.

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.

Q-Learning reinforcement-learning +1

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