Multi-Level GNN Preconditioner for Solving Large Scale Problems

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

Large-scale numerical simulations often come at the expense of daunting computations. High-Performance Computing has enhanced the process, but adapting legacy codes to leverage parallel GPU computations remains challenging. Meanwhile, Machine Learnin

Externí odkaz: http://arxiv.org/abs/2402.08296

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

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

Publikováno v: Machine Learning and the Physical Sciences workshop, NeurIPS 2022, Dec 2022, New-Orleans, United States

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 co

Externí odkaz: http://arxiv.org/abs/2211.11763

DS-GPS : A Deep Statistical Graph Poisson Solver

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

Publikováno v: NeurIPS 2022-Machine Learning and the Physical Sciences, workshop
NeurIPS 2022-Machine Learning and the Physical Sciences, workshop, Dec 2022, New-Orleans, United States

International audience

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______165::1ee1520f0f6d1056f9a0aac13cb2797e
https://inria.hal.science/hal-03864015