1 citations · 3 across the 4 of their papers we have counts for
4 papers
PLAID: A Unified Data Model for Machine Learning on Heterogeneous Physics Simulations
Fabien Casenave, Xavier Roynard, Brian Staber +17
Machine learning-based surrogate models have emerged as a powerful tool to accelerate simulation-driven scientific workflows, but their adoption is limited by the lack of large-sca…
Multi-Level GNN Preconditioner for Solving Large Scale Problems
Matthieu Nastorg, Jean-Marc Gratien, Thibault Faney +3
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 par…
An Implicit GNN Solver for Poisson-like problems
Matthieu Nastorg, Michele Alessandro Bucci, Thibault Faney +3
This paper presents -GNN, a novel Graph Neural Network (GNN) approach for solving the ubiquitous Poisson PDE problems with mixed boundary conditions. By leveraging the Implicit…
DS-GPS : A Deep Statistical Graph Poisson Solver (for faster CFD simulations)
Matthieu Nastorg, Marc Schoenauer, Guillaume Charpiat +3
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…