Work

DS-GPS : A Deep Statistical Graph Poisson Solver (for faster CFD simulations)

Year: 2022

Type: preprint

Abstract: This paper proposes a novel Machine Learning-based approach to solve a Poisson problem with mixed boundary conditions. Leveraging Graph Neural Networks, we develop a model able to process unstructured grids with the advantage of enforcing boundary conditions by design. By directly minimizing the residual of the Poisson equation, the model attempts to learn the physics of the problem without the need for exact solutions, in contrast to most previous data-driven processes where the distance with the available solutions is minimized.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2211.11763

Authors: Nastorg, Matthieu, Schoenauer, Marc, Charpiat, Guillaume, Faney, Thibault, Gratien, Jean-Marc, Bucci, Michele-Alessandro

Institutions: Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, IFP Énergies nouvelles, Laboratoire Interdisciplinaire des Sciences du Numérique

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4309865491

Cites:

Cited by: 1

Related to: 10