no code implementations • 8 Apr 2022 • Hugo Frezat, Julien Le Sommer, Ronan Fablet, Guillaume Balarac, Redouane Lguensat
State-of-the-art strategies address the problem as a supervised learning task and optimize algorithms that predict subgrid fluxes based on information from coarse resolution models.
1 code implementation • 20 Mar 2022 • 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.
no code implementations • 11 Feb 2022 • Said Ouala, Steven L. Brunton, Ananda Pascual, Bertrand Chapron, Fabrice Collard, Lucile Gaultier, Ronan Fablet
The complexity of real-world geophysical systems is often compounded by the fact that the observed measurements depend on hidden variables.
1 code implementation • 12 Nov 2021 • Hugo Frezat, Julien Le Sommer, Ronan Fablet, Guillaume Balarac, Redouane Lguensat
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 simulation (DNS) is impossible.
no code implementations • 7 Oct 2021 • Quentin Febvre, Ronan Fablet, Julien Le Sommer, Clément Ubelmann
The proposed framework significantly outperforms the operational state-of-the-art mapping pipeline and truly benefits from wide-swath data to resolve finer scales on the global map as well as in the SWOT sensor geometry.
1 code implementation • 8 Sep 2021 • Duong Nguyen, Ronan Fablet
Modelling trajectory in general, and vessel trajectory in particular, is a difficult task because of the multimodal and complex nature of motion data.
no code implementations • 18 May 2021 • Noura Dridi, Lucas Drumetz, Ronan Fablet
They are notable for the ability to include a deterministic component of the system and a stochastic one to represent random unknown factors.
no code implementations • 11 May 2021 • Said Ouala, Laurent Debreu, Ananda Pascual, Bertrand Chapron, Fabrice Collard, Lucile Gaultier, Ronan Fablet
Inevitably, a numerical simulation of a differential equation will then always be distinct from a true analytical solution.
1 code implementation • 9 Oct 2020 • Hugo Frezat, Guillaume Balarac, Julien Le Sommer, Ronan Fablet, Redouane Lguensat
In this paper we present a new strategy to model the subgrid-scale scalar flux in a three-dimensional turbulent incompressible flow using physics-informed neural networks (NNs).
1 code implementation • 4 Sep 2020 • Duong Nguyen, Said Ouala, Lucas. Drumetz, Ronan Fablet
The data-driven recovery of the unknown governing equations of dynamical systems has recently received an increasing interest.
no code implementations • 12 Aug 2020 • Duong Nguyen, Matthieu Simonin, Guillaume Hajduch, Rodolphe Vadaine, Cédric Tedeschi, Ronan Fablet
The constant growth of maritime traffic leads to the need of automatic anomaly detection, which has been attracting great research attention.
2 code implementations • 25 Jul 2020 • Ronan Fablet, Bertrand Chapron, Lucas. Drumetz, Etienne Memin, Olivier Pannekoucke, Francois Rousseau
Intriguingly, we also show that the variational models issued from the true Lorenz-63 and Lorenz-96 ODE representations may not lead to the best reconstruction performance.
5 code implementations • 5 Jun 2020 • Ronan Fablet, Lucas. Drumetz, Francois Rousseau
The variational cost and the gradient-based solver are both stated as neural networks using automatic differentiation for the latter.
1 code implementation • 3 May 2020 • Redouane Lguensat, Ronan Fablet, Julien Le Sommer, Sammy Metref, Emmanuel Cosme, Kaouther Ouenniche, Lucas. Drumetz, Jonathan Gula
The upcoming Surface Water Ocean Topography (SWOT) satellite altimetry mission is expected to yield two-dimensional high-resolution measurements of Sea Surface Height (SSH), thus allowing for a better characterization of the mesoscale and submesoscale eddy field.
1 code implementation • 3 Feb 2020 • Olivier Pannekoucke, Ronan Fablet
While deep learning frameworks open avenues in physical science, the design of physically-consistent deep neural network architectures is an open issue.
2 code implementations • 2 Dec 2019 • Duong Nguyen, Rodolphe Vadaine, Guillaume Hajduch, René Garello, Ronan Fablet
Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness.
1 code implementation • 20 Nov 2019 • Redouane Lguensat, Julien Le Sommer, Sammy Metref, Emmanuel Cosme, Ronan Fablet
We introduce a new strategy designed to help physicists discover hidden laws governing dynamical systems.
4 code implementations • 1 Oct 2019 • Ronan Fablet, Lucas. Drumetz, François Rousseau
In this paper, we address the end-to-end learning of representations of signals, images and image sequences from irregularly-sampled data, i. e. when the training data involved missing data.
1 code implementation • 4 Jul 2019 • Said Ouala, Duong Nguyen, Lucas. Drumetz, Bertrand Chapron, Ananda Pascual, Fabrice Collard, Lucile Gaultier, Ronan Fablet
This paper addresses the data-driven identification of latent dynamical representations of partially-observed systems, i. e., dynamical systems for which some components are never observed, with an emphasis on forecasting applications, including long-term asymptotic patterns.
no code implementations • 25 Mar 2019 • Duong Nguyen, Said Ouala, Lucas. Drumetz, Ronan Fablet
To solve for the joint inference of the hidden dynamics and of model parameters, we combine neural-network representations and state-of-the-art assimilation schemes.
1 code implementation • 13 Feb 2019 • Duong Nguyen, Oliver S. Kirsebom, Fábio Frazão, Ronan Fablet, Stan Matwin
In this paper, we adapt Recurrent Neural Networks with Stochastic Layers, which are the state-of-the-art for generating text, music and speech, to the problem of acoustic novelty detection.
Ranked #2 on
Acoustic Novelty Detection
on A3Lab PASCAL CHiME
1 code implementation • 6 Jun 2018 • Duong Nguyen, Rodolphe Vadaine, Guillaume Hajduch, René Garello, Ronan Fablet
In a world of global trading, maritime safety, security and efficiency are crucial issues.
no code implementations • 1 Jun 2018 • Said Ouala, Cedric Herzet, Ronan Fablet
The forecasting and reconstruction of ocean and atmosphere dynamics from satellite observation time series are key challenges.
no code implementations • 24 May 2018 • Francois Rousseau, Ronan Fablet
This paper addresses the understanding and characterization of residual networks (ResNet), which are among the state-of-the-art deep learning architectures for a variety of supervised learning problems.
no code implementations • 19 Dec 2017 • 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 dynamical systems for a wide range of applications, where model-driven models based on ordinary differential equation remain the state-of-the-art approaches.
1 code implementation • 10 Nov 2017 • Redouane Lguensat, Miao Sun, Ronan Fablet, Evan Mason, Pierre Tandeo, Ge Chen
This work presents EddyNet, a deep learning based architecture for automated eddy detection and classification from Sea Surface Height (SSH) maps provided by the Copernicus Marine and Environment Monitoring Service (CMEMS).
no code implementations • 7 Apr 2017 • Manuel López-Radcenco, Ronan Fablet, Abdeldjalil Aïssa-El-Bey, Pierre Ailliot
Super-resolution is a classical problem in image processing, with numerous applications to remote sensing image enhancement.