10 citations · 19 across the 4 of their papers we have counts for
6 papers
Neural Fields for Fast and Scalable Interpolation of Geophysical Ocean Variables
J. Emmanuel Johnson, Redouane Lguensat, Ronan Fablet +2
Optimal Interpolation (OI) is a widely used, highly trusted algorithm for interpolation and reconstruction problems in geosciences. With the influx of more satellite missions, we h…
NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations
Paula Harder, William Jones, Redouane Lguensat +8
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. The gap can be…
Physical invariance in neural networks for subgrid-scale scalar flux modeling
Hugo Frezat, Guillaume Balarac, Julien Le Sommer +2
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). Wh…
Filtering Internal Tides From Wide-Swath Altimeter Data Using Convolutional Neural Networks
Redouane Lguensat, Ronan Fablet, Julien Le Sommer +5
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 a…
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…
Classification Algorithms for Semi-Blind Uplink/Downlink Decoupling in sub-6 GHz/mmWave 5G Networks
Hatim Chergui, Kamel Tourki, Redouane Lguensat +3
Reliability and latency challenges in future mixed sub-6 GHz/millimeter wave (mmWave) fifth generation (5G) cell-free massive multiple-input multiple-output (MIMO) networks is to g…