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2020

Communications

221 - 

Continuous dictionaries meet low-rank tensor approximations

Clément Elvira, Jérémy E Cohen, Cédric Herzet, Rémi Gribonval

iTwist 2020 - International Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, Jun 2020, Nantes, France. pp.1-3.

 

222 - 

Joint learning of variational data assimilation models and solvers This paper addresses variational data assimilation from a learning point of view. Data assimilation aims to reconstruct the time evolution of some state given a series of observations, possibly noisy and irregularly-sampled. Using automatic differentiation tools embedded in deep learning frameworks, we introduce end-to-end neural network (NN) architectures for variational data assimilation. It comprises two key components: a variational model and a gradient-based solver both implemented as neural networks. The latter exploits ideas similar to meta-learning and optimizer learning. A key feature of the proposed end-to-end framework is that we may train the NN models using both supervised and unsupervised strategies. Especially, we may evaluate whether the minimization of the classic definition of variational formulations from ODE-based or PDE-based representations of geophysical dynamics leads to the best reconstruction performance.We report numerical experiments on Lorenz-63 and Lorenz-96 systems for a weak constraint 4D-Var setting with noisy and irregularly-sampled/partial observations. The key features of the proposed neural network framework is two-fold: (i) the learning of fast iterative solvers, which can reach the same minimization performance as a fixed-step gradient descent with only a few tens of iterations, (ii) the significant gain in the reconstruction performance (a relative gain greater than 50%) when considering a supervised solver, i.e. a solver trained to optimize the reconstruction error rather than to minimize the considered variational cost. In this supervised setting, we also show that the joint learning of the variational prior and of the solver significantly outperform NN representations. Intriguingly, the trained representations leading to the best reconstruction performance may lead to significantly worse short-term forecast performance. We believe these results may open new research avenues for the specification of assimilation models and solvers in geoscience, including the design of observation settings to implement learning strategies.

Ronan Fablet, Lucas Drumetz, François Rousseau, Olivier Pannekoucke, Bertrand Chapron, Etienne Mémin

Joint learning of variational data assimilation models and solvers. ECMWF-ESA 2020 - Workshop on Machine Learning for Earth System Observation and Prediction, Oct 2020, Reading, United Kingdom.

 

223 - 

The Fundamental Theorem of Tropical Partial Differential Algebraic Geometry

Sebastian Falkensteiner, Cristhian Emmanuel Garay-Lopez, Mercedes Haiech, Marc Paul Noordman, Zeinab Toghani, François Boulier

ISSAC'20 : International Symposium on Symbolic and Algebraic Computation 2020, Jul 2020, Kalamata, Greece. pp.178-185,

DOI : https://doi.org/10.1145/3373207.3404040

 

224 - 

The New Butterfly Relaxation Method for degenerate optimization problems

Mounir Haddou, Jean-Pierre Dussault, Tangi Migot

IFSOVAA Indo-French Seminar on Optimization, Variational Analysis Applications, Feb 2020, Varanasi, India.

 

225 - 

Generalized Kernel-Based Dynamic Mode Decomposition

Patrick Héas, Cédric Herzet, Benoit Combes

ICASSP 2020 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2020, Barcelona, Spain. pp.3877-3881,

DOI : https://doi.org/10.1109/ICASSP40776.2020.9054594

 

226 - 

Real-time observer from stochastic reduced order model

Matheus Ladvig, Agustin Martin Picard, Valentin Resseguier, Dominique Heitz, Etienne Mémin, Bertrand Chapron

GDR Flow Separation Control, Nov 2020, Poitiers, France.

 

227 - 

Quasi-geostrophic flow under location uncertainty

Long Li, Etienne Mémin, Bertrand Chapron

Seminar of Stochastic Transport in Upper Ocean Dynamics (STUOD) project, May 2020, Rennes, France. pp.1-52.

 

228 - 

Stochastic modeling of the oceanic mesoscale eddies

Long Li, Etienne Mémin, Deremble Bruno

Stochastic Transport in Upper Ocean Dynamics Workshop, Sep 2020, London, United Kingdom.

 

229 - 

Reflection on a methodology for the sensory analysis of complex products: Automotive human-machine interfaces

Muriel Noel, Sébastien Lê, Yvonnick Noel

9th European Conference on Sensory and Consumer Research, Dec 2020, Rotterdam, Netherlands.

 

230 - 

Prediction of fatty acids in the rainbow trout Oncorhynchus mykiss: a Raman scattering spectroscopy approach

E. Prado, Christophe Eklouh-Molinier, Florian Enez, C. Blay, Mathilde Dupont-Nivet, Laurent Labbé, V. Petit, Alain Moréac, G. Taupier, Daniel Guemene, Pierrick Haffray, Geneviève Corraze, David Causeur, Virginie Nazabal

22. International Conference on Transparent Optical Networks (ICTON), Jul 2020, Bari, Italy. pp.1-4,

DOI : https://doi.org/10.1109/ICTON51198.2020.9203514