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06/19/2023
A VAE Approach to Sample Multivariate Extremes
Generating accurate extremes from an observational data set is crucial w...
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03/18/2023
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensive...
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03/16/2023
Reduction of rain-induced errors for wind speed estimation on SAR observations using convolutional neural networks
Synthetic Aperture Radar is known to be able to provide high-resolution ...
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11/23/2022
Inversion of sea surface currents from satellite-derived SST-SSH synergies with 4DVarNets
Satellite altimetry is a unique way for direct observations of sea surfa...
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11/18/2022
Neural Fields for Fast and Scalable Interpolation of Geophysical Ocean Variables
Optimal Interpolation (OI) is a widely used, highly trusted algorithm fo...
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11/17/2022
Deep learning for Lagrangian drift simulation at the sea surface
We address Lagrangian drift simulation in geophysical dynamics and explo...
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08/18/2022
Learning-based estimation of in-situ wind speed from underwater acoustics
Wind speed retrieval at sea surface is of primary importance for scienti...
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07/15/2022
Rain Rate Estimation with SAR using NEXRAD measurements with Convolutional Neural Networks
Remote sensing of rainfall events is critical for both operational and s...
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04/08/2022
A posteriori learning for quasi-geostrophic turbulence parametrization
The use of machine learning to build subgrid parametrizations for climat...
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03/20/2022
Multimodal learning-based inversion models for the space-time reconstruction of satellite-derived geophysical fields
For numerous earth observation applications, one may benefit from variou...
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02/11/2022
Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning
The complexity of real-world geophysical systems is often compounded by ...
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11/12/2021
A posteriori learning of quasi-geostrophic turbulence parametrization: an experiment on integration steps
Modeling the subgrid-scale dynamics of reduced models is a long standing...
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10/07/2021
Joint calibration and mapping of satellite altimetry data using trainable variational models
Satellite radar altimeters are a key source of observation of ocean surf...
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09/08/2021
TrAISformer-A generative transformer for AIS trajectory prediction
Modelling trajectory in general, and vessel trajectory in particular, is...
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05/18/2021
Learning stochastic dynamical systems with neural networks mimicking the Euler-Maruyama scheme
Stochastic differential equations (SDEs) are one of the most important r...
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05/11/2021
Learning Runge-Kutta Integration Schemes for ODE Simulation and Identification
Deriving analytical solutions of ordinary differential equations is usua...
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10/09/2020
Physical invariance in neural networks for subgrid-scale scalar flux modeling
In this paper we present a new strategy to model the subgrid-scale scala...
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09/04/2020
Variational Deep Learning for the Identification and Reconstruction of Chaotic and Stochastic Dynamical Systems from Noisy and Partial Observations
The data-driven recovery of the unknown governing equations of dynamical...
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08/12/2020
Detection of Abnormal Vessel Behaviours from AIS data using GeoTrackNet: from the Laboratory to the Ocean
The constant growth of maritime traffic leads to the need of automatic a...
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07/25/2020
Learning Variational Data Assimilation Models and Solvers
This paper addresses variational data assimilation from a learning point...
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06/05/2020
Joint learning of variational representations and solvers for inverse problems with partially-observed data
Designing appropriate variational regularization schemes is a crucial pa...
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05/03/2020
Filtering Internal Tides From Wide-Swath Altimeter Data Using Convolutional Neural Networks
The upcoming Surface Water Ocean Topography (SWOT) satellite altimetry m...
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02/03/2020
PDE-NetGen 1.0: from symbolic PDE representations of physical processes to trainable neural network representations
Bridging physics and deep learning is a topical challenge. While deep le...
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12/02/2019
GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection
Representing maritime traffic patterns and detecting anomalies from them...
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11/20/2019
Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Models
We introduce a new strategy designed to help physicists discover hidden ...
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10/01/2019
End-to-end learning of energy-based representations for irregularly-sampled signals and images
For numerous domains, including for instance earth observation, medical ...
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07/04/2019
Learning Latent Dynamics for Partially-Observed Chaotic Systems
This paper addresses the data-driven identification of latent dynamical ...
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03/25/2019
EM-like Learning Chaotic Dynamics from Noisy and Partial Observations
The identification of the governing equations of chaotic dynamical syste...
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02/13/2019
Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection
In this paper, we adapt Recurrent Neural Networks with Stochastic Layers...
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06/06/2018
Multi-task Learning for Maritime Traffic Surveillance from AIS Data Streams
In a world of global trading, maritime safety, security and efficiency a...
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06/01/2018
Sea surface temperature prediction and reconstruction using patch-level neural network representations
The forecasting and reconstruction of ocean and atmosphere dynamics from...
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05/24/2018
Residual Networks as Geodesic Flows of Diffeomorphisms
This paper addresses the understanding and characterization of residual ...
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12/19/2017
Bilinear residual Neural Network for the identification and forecasting of dynamical systems
Due to the increasing availability of large-scale observation and simula...
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11/10/2017
EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies
This work presents EddyNet, a deep learning based architecture for autom...
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04/07/2017