24 citations · 78 across the 11 of their papers we have counts for
18 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…
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…
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…
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…