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Found 6 papers, 0 papers with code
Multi-Level GNN Preconditioner for Solving Large Scale Problems
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.
An Implicit GNN Solver for Poisson-like problems
This paper presents $\Psi$-GNN, a novel Graph Neural Network (GNN) approach for solving the ubiquitous Poisson PDE problems with mixed boundary conditions.
DS-GPS : A Deep Statistical Graph Poisson Solver (for faster CFD simulations)
This paper proposes a novel Machine Learning-based approach to solve a Poisson problem with mixed boundary conditions.
HMOE: Hypernetwork-based Mixture of Experts for Domain Generalization
This paper presents a novel DG method, called HMOE: Hypernetwork-based Mixture of Experts (MoE), which does not rely on domain labels and is more interpretable.
Machine Learning model for gas-liquid interface reconstruction in CFD numerical simulations
The volume of fluid (VoF) method is widely used in multi-phase flow simulations to track and locate the interface between two immiscible fluids.
PTFlash : A deep learning framework for isothermal two-phase equilibrium calculations
Phase equilibrium calculations are an essential part of numerical simulations of multi-component multi-phase flow in porous media, accounting for the largest share of the computational time.
