Langages de descriptions ou langages d'action ?

Parmi les langages crées de toutes pièces par les humains pour un usage particulier, certains, tel le langage de la logique, sont des langages de descriptions et d'autres, tels les langages de programmation, des langages d'action. Qu'en est-il des langages utilisés en science, du langage des partitions ou des langages chorégraphiques ?

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Coordonées et info personnelles :

Gilles Dowek
Chercheur à Inria et professeur attaché à l'ENS Paris-Saclay.

Web: http://www.lsv.fr/~dowek/index.html.fr

Neural networks & dynamical systems: dealing with partially-observed systems

Learning techniques and data-driven approaches become relevant alternatives to classical model-driven approaches for a large number of application domains, including for the study of phenomena governed by physical laws. They offer new means to take advantage of the potential of observation and / or simulation big data.

In this talk, we will discuss data-driven strategies for the identification, simulation and reconstruction of dynamical systems with illustrations on ocean monitoring applications (e.g., reconstruction of sea surface tracers, maritime traffic surveillance). We will specifically address how neural networks can provide novel means for the data-driven identification of representations of dynamical systems, which are imperfectly observed (e.g., noisy data, partial observation, irregular sampling..). We might further discuss the relevance of dynamical system theory for the understanding of state-of-the-art neural networks, especially residual nets.

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Références:

R. Fablet, P. Viet, R. Lguensat. Data-driven Methods for Spatio-Temporal Interpolation of Sea Surface Temperature Images. IEEE TCI, 2017.

R. Fablet, L. Drumetz, F. Rousseau. End-to-end learning of energy-based representations for irregularly-sampled signals and images. Arxiv, 2019.

R. Lguensat, P. Tandeo, (...), R. Fablet. The Analog Data Assimilation. MWR 2017.

V.D. Nguyen, R. Vadaine, G. Hajduch, R. Garello, R. Fablet. Multi-task Learning for Maritime Traffic Surveillance from AIS Data Streams. IEEE DSAA, 2018.

V.D. Nguyen, S. Ouala, L. Drumetz, R. Fablet. EM-like Learning Chaotic Dynamics from Noisy and Partial Observations. arXiv preprint arXiv:1903.10335. 2019.

S. Ouala, C. Herzet, (...) R. Fablet. Learning stochastic representations of geophysical dynamics. IEEE ICASSP 2019.

S. Ouala, A. Pascual, R. Fablet. Residual Integration Network. IEEE ICASSP 2019.

S. Ouala, V.D. Nguyen, (...), R. Fablet. Learning Latent Dynamics for Partially Observed Systems. Arxiv, 2019.

M. Lopez Radcenco, A. Pascual, (...), R. Fablet. Analog Data Assimilation of Alongtrack Nadir and Wideswath SWOT Altimetry Observations in the Western Mediterranean Sea. IEEE JSTARS, 2019.

F. Rousseau, L. Drumetz, R. Fablet. Residual Networks as Geodesic Flows of Diffeomorphisms. JMIV, 2019.

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Coordonées et info personnelles :

Ronan Fablet
Professeur à l'IMT Atlantique (anciennement, IMT/Telecom Bretagne)
UMR 6285 LabSTICC, TOMS (Statistical Signal Processing and Remote Sensing)
Technopole Brest-Iroise - CS 83818

Web: https://rfablet.github.io
ResearchGate: https://www.researchgate.net/profile/Ronan_Fablet
Mail:

Identification de systèmes dynamiques par approches de type DMD

Dans cette présentation, on discutera de l’identification de systèmes dynamiques à partir de données, en utilisant un ensemble de briques ‘élémentaires’ : POD, DMD et EDMD, ANN. Plusieurs exemples seront abordés (contrôle switché d’un système thermique, systèmes proies-prédateurs). Un zoom sera fait sur la capacité du modèle recherché à respecter un invariant physique connu, conduisant à des équations de Lyapunov.

Coordonées et info personnelles :

Florian De Vuyst
Professeur des Universités
Directeur du Laboratoire LMAC EA 2222
Alliance Sorbonne Université / UTC
Département Génie Informatique
CS 60319 - 57 Avenue de Landshut 60203 COMPIEGNE CEDEX

ResearchGate: https://www.researchgate.net/profile/Florian_De_Vuyst

Problème a 3/N corps et missions spatiales vers les points de Lagrange

Coordonées et info personnelles :

Éric Joffre
Spécialiste des mission inter-planétaires
Airbus
Gunnels Wood Road, Stevenage SG1 2AS, UK

ResearchGate: https://www.researchgate.net/profile/Eric_Joffre

Data-driven discovery and uncertainty quantification of turbulence models for Fluid Dynamics

Computational Fluid Dynamics (CFD) models have become a fundamental tool for preliminary and advanced Aerodynamic design and optimization. So-called high-fidelity CFD models like DNS (Direct Numerical Simulation) or LES (Large Eddy Simulation) remain very costly for complex high-Reynolds flows, and lower fidelity methods, relying on the Reynolds-Averaged Navier-Stokes (RANS) equations supplemented by a turbulence model, represent the workhorse for engineering flow simulations. Unfortunately, RANS models are characterized by numerous flaws, intrinsic to the simplified description of turbulence they rely on. Additionally, despite much theoretical effort for developing turbulence models on a physically sound basis, these still rely on a significant amount of empiricism and expert judgement both for defining the model mathematical structure and for calibrating the associated closure coefficients. In recent years, an exponentially growing mess of literature has attempted to develop data-driven RANS models. Specifically, the use of machine learning techniques for generating turbulence models seem attractive, but it is also risky due the scarcity of data and the very large dimensionality of fluid flow problems. In both cases, obtaining reliable estimates of uncertainty bounds of the model outcomes appears even more compelling than the accuracy of the prediction itself. In this talk we present recent advances on the quantification and reduction of model uncertainties by using Bayesian statistical inference techniques and about the data-driven discovery of improved turbulence models with quantified uncertainties, as well as applications to some flow configurations of interest in internal and external Aerodynamics. Such approaches are quite general and can be used for quantifying and reducing model uncertainties in other fields of Science and Engineering.

Coordonées et info personnelles :

Paola Cinella
Professeur
Laboratoire DynFluid de dynamique des fluides, ENSAM
ResearchGate: https://www.researchgate.net/profile/Paola_Cinnella