Search Results for author: Lucas. Drumetz

Found 8 papers, 7 papers with code

Variational Deep Learning for the Identification and Reconstruction of Chaotic and Stochastic Dynamical Systems from Noisy and Partial Observations

1 code implementation4 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.

Learning Variational Data Assimilation Models and Solvers

2 code implementations25 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.

Joint learning of variational representations and solvers for inverse problems with partially-observed data

5 code implementations5 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.

Image Inpainting Time Series +1

Filtering Internal Tides From Wide-Swath Altimeter Data Using Convolutional Neural Networks

1 code implementation3 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.

Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review

1 code implementation21 Jan 2020 Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard, Jocelyn Chanussot, Lucas. Drumetz, Jean-Yves Tourneret, Alina Zare, Christian Jutten

The spectral signatures of the materials contained in hyperspectral images, also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an image.

End-to-end learning of energy-based representations for irregularly-sampled signals and images

4 code implementations1 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.

Time Series Time Series Analysis

Learning Latent Dynamics for Partially-Observed Chaotic Systems

1 code implementation4 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.

EM-like Learning Chaotic Dynamics from Noisy and Partial Observations

no code implementations25 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.

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