188 citations · 762 across the 33 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
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-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…
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…
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…
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…
Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Models
Redouane Lguensat, Julien Le Sommer, Sammy Metref +2
We introduce a new strategy designed to help physicists discover hidden laws governing dynamical systems. We propose to use machine learning automatic differentiation libraries to…
EM-like Learning Chaotic Dynamics from Noisy and Partial Observations
Duong Nguyen, Said Ouala, Lucas Drumetz +1
The identification of the governing equations of chaotic dynamical systems from data has recently emerged as a hot topic. While the seminal work by Brunton et al. reported proof-of…
A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams
Duong Nguyen, Rodolphe Vadaine, Guillaume Hajduch +2
In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Ide…
Bilinear residual Neural Network for the identification and forecasting of dynamical systems
Ronan Fablet, Said Ouala, Cedric Herzet
Due to the increasing availability of large-scale observation and simulation datasets, data-driven representations arise as efficient and relevant computation representations of dy…