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Lucas Meyer

Lundi 11 Mars 2024

Deep Learning for Numerical Simulation at Scale


Many engineering and scientific applications rely on large ensembles of numerical simulations that reproduce faithfully complex phenomena. These ensembles are limited by long computation time and finite storage capacity. These issues led the development of high performance computing (HPC) and reduced order modeling techniques. Recently, the success of deep learning led the scientific community to consider its use for accelerating numerical simulations. However, deep surrogate models require many simulations for training. This approach thus suffers the same limitation that motivates its development in first instance : to produce a representative training dataset of faithful simulations is tedious. We propose an online training framework for deep surrogate models that generate simulation data on-the-fly by leveraging HPC resources.

Date et lieu

Lundi 11 Mars 2024 à 14:30

Auditorium of EDF Lab Paris-Saclay

Composition du jury

professor, Sorbonne Université
research leader Argonne National Laboratory
Guillaume CHARPIAT
research leader, INRIA de Saclay
research director, INRIA de l'UGA
professor, Université Grenoble Alpes
Michele Alessandro BUCCI
doctor, engineer R&D Safran Tech
Alejandro RIBES
docteur, engineer research, R&D EDF expert

Publié le 18 mars 2024

Mis à jour le 18 mars 2024