1 citations · 3 across the 6 of their papers we have counts for
6 papers
A Hyper-Reduced Neural Network-Augmented Semi-smooth Newton Method for Nonlinear Parametric Variational Inequalities
Sofiane Ezzehi, Virginie Ehrlacher, Guillaume Enchéry +1
We propose a model order reduction framework for nonlinear parametrized variational inequalities arising in computational mechanics. The high-dimensional model is written in mixed…
Neural Network for Subgrid Turbulence Modeling for Large Eddy Simulations
Eduardo Vital, Jean-Marc Gratien, Yassine Ayoun +2
When simulating multiscale systems, where some fields cannot be fully prescribed despite their effects on the simulation's accuracy, closure models are needed. This phenomenon is o…
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…
Machine Learning model for gas-liquid interface reconstruction in CFD numerical simulations
Tamon Nakano, Alessandro Michele Bucci, Jean-Marc Gratien +2
The volume of fluid (VoF) method is widely used in multi-phase flow simulations to track and locate the interface between two immiscible fluids. A major bottleneck of the VoF metho…