99 citations · 337 across the 25 of their papers we have counts for
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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…