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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.
Enhancing Data-Assimilation in CFD using Graph Neural Networks
We present a novel machine learning approach for data assimilation applied in fluid mechanics, based on adjoint-optimization augmented by Graph Neural Networks (GNNs) models.
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.
Continuous Methods : Adaptively intrusive reduced order model closure
Reduced order modeling methods are often used as a mean to reduce simulation costs in industrial applications.
Continuous Methods : Hamiltonian Domain Translation
This paper proposes a novel approach to domain translation.
CD-ROM: Complemented Deep-Reduced Order Model
Model order reduction through the POD-Galerkin method can lead to dramatic gains in terms of computational efficiency in solving physical problems.
