b'Ronan Fablet'

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Featured Co-authors

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53 publications
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42 publications
Lucas Drumetz
24 publications
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22 publications
Bertrand Iooss
19 publications
Cédric Herzet
18 publications
Weiping Ding
17 publications
Rossella Arcucci
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Marc Bocquet
16 publications
Philippe Naveau
16 publications

research

∙ 06/19/2023

A VAE Approach to Sample Multivariate Extremes

Generating accurate extremes from an observational data set is crucial w...

0 Nicolas Lafon, et al. ∙

research

∙ 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...

0 Sibo Cheng, et al. ∙

research

∙ 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 ...

0 Aurélien Colin, et al. ∙

research

∙ 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...

0 Ronan Fablet, et al. ∙

research

∙ 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...

12 J. Emmanuel Johnson, et al. ∙

research

∙ 11/17/2022

Deep learning for Lagrangian drift simulation at the sea surface

We address Lagrangian drift simulation in geophysical dynamics and explo...

0 Daria Botvynko, et al. ∙

research

∙ 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...

0 Matteo Zambra, et al. ∙

research

∙ 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...

2 Aurélien Colin, et al. ∙

research

∙ 04/08/2022

A posteriori learning for quasi-geostrophic turbulence parametrization

The use of machine learning to build subgrid parametrizations for climat...

0 Hugo Frezat, et al. ∙

research

∙ 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...

0 Ronan Fablet, et al. ∙

research

∙ 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 ...

9 Said Ouala, et al. ∙

research

∙ 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...

0 Hugo Frezat, et al. ∙

research

∙ 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...

0 Quentin Febvre, et al. ∙

research

∙ 09/08/2021

TrAISformer-A generative transformer for AIS trajectory prediction

Modelling trajectory in general, and vessel trajectory in particular, is...

0 Duong Nguyen, et al. ∙

research

∙ 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...

0 Noura Dridi, et al. ∙

research

∙ 05/11/2021

Learning Runge-Kutta Integration Schemes for ODE Simulation and Identification

Deriving analytical solutions of ordinary differential equations is usua...

0 Said Ouala, et al. ∙

research

∙ 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...

0 Hugo Frezat, et al. ∙

research

∙ 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...

44 Duong Nguyen, et al. ∙

research

∙ 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...

0 Duong Nguyen, et al. ∙

research

∙ 07/25/2020

Learning Variational Data Assimilation Models and Solvers

This paper addresses variational data assimilation from a learning point...

0 Ronan Fablet, et al. ∙

research

∙ 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...

0 Ronan Fablet, et al. ∙

research

∙ 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...

0 Redouane Lguensat, et al. ∙

research

∙ 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...

0 Olivier Pannekoucke, et al. ∙

research

∙ 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...

17 Duong Nguyen, et al. ∙

research

∙ 11/20/2019

Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Models

We introduce a new strategy designed to help physicists discover hidden ...

0 Redouane Lguensat, et al. ∙

research

∙ 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 ...

0 Ronan Fablet, et al. ∙

research

∙ 07/04/2019

Learning Latent Dynamics for Partially-Observed Chaotic Systems

This paper addresses the data-driven identification of latent dynamical ...

0 Said Ouala, et al. ∙

research

∙ 03/25/2019

EM-like Learning Chaotic Dynamics from Noisy and Partial Observations

The identification of the governing equations of chaotic dynamical syste...

0 Duong Nguyen, et al. ∙

research

∙ 02/13/2019

Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection

In this paper, we adapt Recurrent Neural Networks with Stochastic Layers...

0 Duong Nguyen, et al. ∙

research

∙ 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...

0 Duong Nguyen, et al. ∙

research

∙ 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...

0 Said Ouala, et al. ∙

research

∙ 05/24/2018

Residual Networks as Geodesic Flows of Diffeomorphisms

This paper addresses the understanding and characterization of residual ...

0 Francois Rousseau, et al. ∙

research

∙ 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...

0 Ronan Fablet, et al. ∙

research

∙ 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...

0 Redouane Lguensat, et al. ∙

research

∙ 04/07/2017

Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data

Super-resolution is a classical problem in image processing, with numero...

0 Manuel López-Radcenco, et al. ∙

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