Search Results for author: Florent Bonnet

Found 5 papers, 2 papers with code

ML4PhySim : Machine Learning for Physical Simulations Challenge (The airfoil design)

no code implementations3 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).

Physical Simulations

An operator preconditioning perspective on training in physics-informed machine learning

no code implementations9 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).

Physics-informed machine learning

Graph Neural Networks for Airfoil Design

no code implementations9 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.

AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions

3 code implementations15 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.

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