Pages that link to "Item:Q2675603"
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The following pages link to Machine learning based refinement strategies for polyhedral grids with applications to virtual element and polyhedral discontinuous Galerkin methods (Q2675603):
Displaying 11 items.
- Accelerating algebraic multigrid methods via artificial neural networks (Q2679755) (← links)
- Machine Learning Surrogate Modeling for Meshless Methods: Leveraging Universal Approximation (Q6048309) (← links)
- Quasi-optimal \textit{hp}-finite element refinements towards singularities via deep neural network prediction (Q6103655) (← links)
- Agglomeration of polygonal grids using graph neural networks with applications to multigrid solvers (Q6144203) (← links)
- Learning Robust Marking Policies for Adaptive Mesh Refinement (Q6189171) (← links)
- Learning adaptive coarse basis functions of FETI-DP (Q6198156) (← links)
- Adaptive discontinuous Galerkin finite element methods for the Allen-Cahn equation on polygonal meshes (Q6202805) (← links)
- Machine Learning based refinement strategies for polyhedral grids with applications to Virtual Element and polyhedral Discontinuous Galerkin methods (Q6392141) (← links)
- Discontinuous Galerkin method for the coupled dual-porosity-Brinkman model (Q6543626) (← links)
- Higher-order adaptive virtual element methods with contraction properties (Q6581238) (← links)
- Mesh optimization for the virtual element method: how small can an agglomerated mesh become? (Q6670730) (← links)
