188 citations · 762 across the 33 of their papers we have counts for
35 papers
OceanBench: The Sea Surface Height Edition
J. Emmanuel Johnson, Quentin Febvre, Anastasia Gorbunova +4
The ocean profoundly influences human activities and plays a critical role in climate regulation. Our understanding has improved over the last decades with the advent of satellite…
Training neural mapping schemes for satellite altimetry with simulation data
Quentin Febvre, Julien Le Sommer, Clément Ubelmann +1
Satellite altimetry combined with data assimilation and optimal interpolation schemes have deeply renewed our ability to monitor sea surface dynamics. Recently, deep learning (DL)…
A VAE Approach to Sample Multivariate Extremes
Nicolas Lafon, Philippe Naveau, Ronan Fablet
Generating accurate extremes from an observational data set is crucial when seeking to estimate risks associated with the occurrence of future extremes which could be larger than t…
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Sibo Cheng, Cesar Quilodran-Casas, Said Ouala +14
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical a…
Reduction of rain-induced errors for wind speed estimation on SAR observations using convolutional neural networks
Aurélien Colin, Pierre Tandeo, Charles Peureux +2
Synthetic Aperture Radar is known to be able to provide high-resolution estimates of surface wind speed. These estimates usually rely on a Geophysical Model Function (GMF) that has…
Scale-aware neural calibration for wide swath altimetry observations
Quentin Febvre, Clément Ubelmann, Julien Le Sommer +1
Sea surface height (SSH) is a key geophysical parameter for monitoring and studying meso-scale surface ocean dynamics. For several decades, the mapping of SSH products at regional…
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…
Deep learning for Lagrangian drift simulation at the sea surface
Daria Botvynko, Carlos Granero-Belinchon, Simon Van Gennip +2
We address Lagrangian drift simulation in geophysical dynamics and explore deep learning approaches to overcome known limitations of state-of-the-art model-based and Markovian appr…
Learning Neural Optimal Interpolation Models and Solvers
Maxime Beauchamp, Joseph Thompson, Hugo Georgenthum +2
The reconstruction of gap-free signals from observation data is a critical challenge for numerous application domains, such as geoscience and space-based earth observation, when th…
4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry
Maxime Beauchamp, Quentin Febvre, Hugo Georgentum +1
The reconstruction of sea surface currents from satellite altimeter data is a key challenge in spatial oceanography, especially with the upcoming wide-swath SWOT (Surface Ocean and…
Inversion of sea surface currents from satellite-derived SST-SSH synergies with 4DVarNets
Ronan Fablet, Bertrand Chapron, Julien Le Sommer +1
Satellite altimetry is a unique way for direct observations of sea surface dynamics. This is however limited to the surface-constrained geostrophic component of sea surface velocit…
CLOINet: Ocean state reconstructions through remote-sensing, in-situ sparse observations and Deep Learning
Eugenio Cutolo, Ananda Pascual, Simon Ruiz +2
Combining remote-sensing data with in-situ observations to achieve a comprehensive 3D reconstruction of the ocean state presents significant challenges for traditional interpolatio…
Learning-based estimation of in-situ wind speed from underwater acoustics
Matteo Zambra, Dorian Cazau, Nicolas Farrugia +4
Wind speed retrieval at sea surface is of primary importance for scientific and operational applications. Besides weather models, in-situ measurements and remote sensing technologi…
Multimodal 4DVarNets for the reconstruction of sea surface dynamics from SST-SSH synergies
Ronan Fablet, Quentin Febvre, Bertrand Chapron
Due to the irregular space-time sampling of sea surface observations, the reconstruction of sea surface dynamics is a challenging inverse problem. While satellite altimetry provide…
A posteriori learning for quasi-geostrophic turbulence parametrization
Hugo Frezat, Julien Le Sommer, Ronan Fablet +2
The use of machine learning to build subgrid parametrizations for climate models is receiving growing attention. State-of-the-art strategies address the problem as a supervised lea…
Multimodal learning-based inversion models for the space-time reconstruction of satellite-derived geophysical fields
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. A variety of s…
Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning
Said Ouala, Steven L. Brunton, Ananda Pascual +4
The complexity of real-world geophysical systems is often compounded by the fact that the observed measurements depend on hidden variables. These latent variables include unresolve…
A posteriori learning of quasi-geostrophic turbulence parametrization: an experiment on integration steps
Hugo Frezat, Julien Le Sommer, Ronan Fablet +2
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 sim…
Joint calibration and mapping of satellite altimetry data using trainable variational models
Quentin Febvre, Ronan Fablet, Julien Le Sommer +1
Satellite radar altimeters are a key source of observation of ocean surface dynamics. However, current sensor technology and mapping techniques do not yet allow to systematically r…
TrAISformer -- A Transformer Network with Sparse Augmented Data Representation and Cross Entropy Loss for AIS-based Vessel Trajectory Prediction
Duong Nguyen, Ronan Fablet
Vessel trajectory prediction plays a pivotal role in numerous maritime applications and services. While the Automatic Identification System (AIS) offers a rich source of informatio…
Learning stochastic dynamical systems with neural networks mimicking the Euler-Maruyama scheme
Noura Dridi, Lucas Drumetz, Ronan Fablet
Stochastic differential equations (SDEs) are one of the most important representations of dynamical systems. They are notable for the ability to include a deterministic component o…
Learning Runge-Kutta Integration Schemes for ODE Simulation and Identification
Said Ouala, Laurent Debreu, Ananda Pascual +4
Deriving analytical solutions of ordinary differential equations is usually restricted to a small subset of problems and numerical techniques are considered. Inevitably, a numerica…
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…
Variational Deep Learning for the Identification and Reconstruction of Chaotic and Stochastic Dynamical Systems from Noisy and Partial Observations
Duong Nguyen, Said Ouala, Lucas Drumetz +1
The data-driven recovery of the unknown governing equations of dynamical systems has recently received an increasing interest. However, the identification of governing equations re…