no code implementations • 13 Feb 2024 • Matthieu Nastorg, Jean-Marc Gratien, Thibault Faney, Michele Alessandro Bucci, Guillaume Charpiat, Marc Schoenauer
The proposed GNN-based preconditioner is used to enhance the efficiency of a Krylov method, resulting in a hybrid solver that can converge with any desired level of accuracy.
no code implementations • 6 Feb 2023 • Matthieu Nastorg, Michele Alessandro Bucci, Thibault Faney, Jean-Marc Gratien, Guillaume Charpiat, Marc Schoenauer
This paper presents $\Psi$-GNN, a novel Graph Neural Network (GNN) approach for solving the ubiquitous Poisson PDE problems with mixed boundary conditions.
no code implementations • 21 Nov 2022 • Matthieu Nastorg, Marc Schoenauer, Guillaume Charpiat, Thibault Faney, Jean-Marc Gratien, Michele-Alessandro Bucci
This paper proposes a novel Machine Learning-based approach to solve a Poisson problem with mixed boundary conditions.
