130 citations · 290 across the 26 of their papers we have counts for
26 papers
Take It or Leave It: Intent-Controlled Partial Optimal Transport
Salil Parth Tripathi, Bertrand Chapron, Fabrice Collard +2
While optimal transport (OT) enforces a rigid constraint by requiring two measures to be matched exactly, partial optimal transport relaxes this requirement by allowing mass to rem…
Optimal sensor placement for the reconstruction of ocean states using differentiable Gumbel-Softmax sampling operator
Oscar Chapron, Ronan Fablet, Yann Stéphan
Accurately reconstructing and forecasting ocean fields from sparse observations is critical for both operational and scientific purposes. Optimizing sensor placement to maximize re…
Impact of geophysical fields on Deep Learning-based Lagrangian drift simulations
Daria Botvynko, Carlos Granero-Belinchon, Simon Van Gennip +2
We assess the influence of different Eulerian geophysical input fields on Lagrangian drift simulations using DriftNet, a learning-based method designed to simulate Lagrangian drift…
Static and auto-regressive neural emulation of phytoplankton biomass dynamics from physical predictors in the global ocean
Mahima Lakra, Ronan Fablet, Lucas Drumetz +2
Phytoplankton is the basis of marine food webs, driving both ecological processes and global biogeochemical cycles. Despite their ecological and climatic significance, accurately s…
Discovering Data Manifold Geometry through Geometric Properties
David Vigouroux, Lucas Drumetz, Ronan Fablet +1
A prevailing paradigm in modern representation learning is the map-first approach, in which a representation map is learned from reconstruction, embedding, or task objectives. At t…
Majorization-Minimization Networks for Inverse Problems: An Application to EEG Imaging
Le Minh Triet Tran, Sarah Reynaud, Ronan Fablet +3
Inverse problems are often ill-posed and require optimization schemes with strong stability and convergence guarantees. While learning-based approaches such as deep unrolling and m…
Neural ocean forecasting from sparse satellite-derived observations: a case-study for SSH dynamics and altimetry data
Daria Botvynko, Pierre Haslée, Lucile Gaultier +5
We present an end-to-end deep learning framework for short-term forecasting of global sea surface dynamics based on sparse satellite altimetry data. Building on two state-of-the-ar…
Towards fully differentiable neural ocean model with Veros
Etienne Meunier, Said Ouala, Hugo Frezat +2
We present a differentiable extension of the VEROS ocean model, enabling automatic differentiation through its dynamical core. We describe the key modifications required to make th…
Simulation-informed deep learning for enhanced SWOT observations of fine-scale ocean dynamics
Eugenio Cutolo, Carlos Granero-Belinchon, Ptashanna Thiraux +2
Oceanic processes at fine scales are crucial yet difficult to observe accurately due to limitations in satellite and in-situ measurements. The Surface Water and Ocean Topography (S…
Generalization performance of neural mapping schemes for the space-time interpolation of satellite-derived ocean colour datasets
Thi Thuy Nga Nguyen, Clément Dorffer, Frédéric Jourdin +1
Neural mapping schemes have become appealing approaches to deliver gap-free satellite-derived products for sea surface tracers. The generalization performance of these learning-bas…
Observation-only learning of neural mapping schemes for gappy satellite-derived ocean colour parameters
Clément Dorffer, Frédéric Jourdin, Thi Thuy Nga Nguyen +3
Monitoring optical properties of coastal and open ocean waters is crucial to assessing the health of marine ecosystems. Deep learning offers a promising approach to address these e…
Neural Incremental Data Assimilation
Matthieu Blanke, Ronan Fablet, Marc Lelarge
Data assimilation is a central problem in many geophysical applications, such as weather forecasting. It aims to estimate the state of a potentially large system, such as the atmos…
Neural variational Data Assimilation with Uncertainty Quantification using SPDE priors
Maxime Beauchamp, Ronan Fablet, Simon Benaichouche +3
The spatio-temporal interpolation of large geophysical datasets has historically been addressed by Optimal Interpolation (OI) and more sophisticated equation-based or data-driven D…
Multi-Modal Learning-based Reconstruction of High-Resolution Spatial Wind Speed Fields
Matteo Zambra, Nicolas Farrugia, Dorian Cazau +2
Wind speed at sea surface is a key quantity for a variety of scientific applications and human activities. Due to the non-linearity of the phenomenon, a complete description of suc…
Online Calibration of Deep Learning Sub-Models for Hybrid Numerical Modeling Systems
Said Ouala, Bertrand Chapron, Fabrice Collard +2
Artificial intelligence and deep learning are currently reshaping numerical simulation frameworks by introducing new modeling capabilities. These frameworks are extensively investi…
Neural SPDE solver for uncertainty quantification in high-dimensional space-time dynamics
Maxime Beauchamp, Ronan Fablet, Hugo Georgenthum
Historically, the interpolation of large geophysical datasets has been tackled using methods like Optimal Interpolation (OI) or model-based data assimilation schemes. However, the…
Gradient-free online learning of subgrid-scale dynamics with neural emulators
Hugo Frezat, Ronan Fablet, Guillaume Balarac +1
In this paper, we propose a generic algorithm to train machine learning-based subgrid parametrizations online, i.e., with loss functions, but for non-differ…
Multi-Path Long-Term Vessel Trajectories Forecasting with Probabilistic Feature Fusion for Problem Shifting
Gabriel Spadon, Jay Kumar, Derek Eden +6
This paper addresses the challenge of boosting the precision of multi-path long-term vessel trajectory forecasting on engineered sequences of Automatic Identification System (AIS)…
Rain regime segmentation of Sentinel-1 observation learning from NEXRAD collocations with Convolution Neural Networks
Aurélien Colin, Pierre Tandeo, Charles Peureux +3
Remote sensing of rainfall events is critical for both operational and scientific needs, including for example weather forecasting, extreme flood mitigation, water cycle monitoring…
Detection of Abnormal Vessel Behaviours from AIS data using GeoTrackNet: from the Laboratory to the Ocean
Duong Nguyen, Matthieu Simonin, Guillaume Hajduch +3
The constant growth of maritime traffic leads to the need of automatic anomaly detection, which has been attracting great research attention. Information provided by AIS (Automatic…
Learning Variational Data Assimilation Models and Solvers
Ronan Fablet, Bertrand Chapron, Lucas. Drumetz +3
This paper addresses variational data assimilation from a learning point of view. Data assimilation aims to reconstruct the time evolution of some state given a series of observati…
Joint learning of variational representations and solvers for inverse problems with partially-observed data
Ronan Fablet, Lucas Drumetz, Francois Rousseau
Designing appropriate variational regularization schemes is a crucial part of solving inverse problems, making them better-posed and guaranteeing that the solution of the associate…
PDE-NetGen 1.0: from symbolic PDE representations of physical processes to trainable neural network representations
Olivier Pannekoucke, Ronan Fablet
Bridging physics and deep learning is a topical challenge. While deep learning frameworks open avenues in physical science, the design of physically-consistent deep neural network…
GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection
Duong Nguyen, Rodolphe Vadaine, Guillaume Hajduch +2
Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness. We propose a novel approach -- referred to…