Séminaire de Mécanique d'Orsay

Le Jeudi 24 février 2022 à 14h00 - Salle de conférences du LIMSI Bât. 507

Data-driven and equation informed tools for turbulent reconstruction and classification.

Luca Biferale
Dept. of Physics, University of Rome, Tor Vergata (on leave)

Data-driven and equation-informed tools to model small-scales, high-frequencies fluctuations in complex flows and to reconstruct large-scale features in gappy-data are presented. Recent implementations of Generative Adversarial Networks to assimilate turbulent data of rotating flows [1] are compared against Principal Orthogonal Decompositions and Nudging [2-3]. My personal understanding of open challenges towards a quantitative-AI for fluid dynamics applications is also presented. [1] A. Alexakis and L. Biferale. Cascades and Transitions in Turbulence. Phys. Rep. 767, 1 (2018). [2] M Buzzicotti, F Bonaccorso, PC Di Leoni, L Biferale. Reconstruction of turbulent data with deep generative models for semantic inpainting from TURB-Rot database. Physical Review Fluids 6 (5), 050503 (2021) [3] P. Clark Di Leoni, A. Mazzino, and L. Biferale. Synchronization to big-data: Nudging the Navier-Stokes equations for data-assimilation of Turbulence flows. Phys. Rev. X 10, 011023 (2020)

Accès Salle de conférences du LIMSI Bât. 507