130 citations · 290 across the 26 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
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…
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)…
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…
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…