no code implementations • 3 Mar 2024 • Mouadh Yagoubi, Milad Leyli-Abadi, David Danan, Jean-Patrick Brunet, Jocelyn Ahmed Mazari, Florent Bonnet, Asma Farjallah, Marc Schoenauer, Patrick Gallinari
The aim of this competition is to encourage the development of new ML techniques to solve physical problems using a unified evaluation framework proposed recently, called Learning Industrial Physical Simulations (LIPS).
no code implementations • 9 Oct 2023 • Tim De Ryck, Florent Bonnet, Siddhartha Mishra, Emmanuel de Bézenac
In this paper, we investigate the behavior of gradient descent algorithms in physics-informed machine learning methods like PINNs, which minimize residuals connected to partial differential equations (PDEs).
no code implementations • 9 May 2023 • Florent Bonnet
However, the resolutions of harder PDE such as Navier-Stokes equations are still a challenging task and most of the work done on the latter concentrate either on simulating the flow around simple geometries or on qualitative results that looks physical for design purpose.
3 code implementations • 15 Dec 2022 • Florent Bonnet, Ahmed Jocelyn Mazari, Paola Cinnella, Patrick Gallinari
Surrogate models are necessary to optimize meaningful quantities in physical dynamics as their recursive numerical resolutions are often prohibitively expensive.
1 code implementation • 29 Jun 2022 • Florent Bonnet, Jocelyn Ahmed Mazari, Thibaut Munzer, Pierre Yser, Patrick Gallinari
Recent progress in \emph{Geometric Deep Learning} (GDL) has shown its potential to provide powerful data-driven models.