Machine learning based refinement strategies for polyhedral grids with applications to virtual element and polyhedral discontinuous Galerkin methods
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Publication:2675603
DOI10.1016/j.jcp.2022.111531OpenAlexW4226168322WikidataQ114163205 ScholiaQ114163205MaRDI QIDQ2675603
E. Manuzzi, Paola Francesca Antonietti, Franco Dassi
Publication date: 24 September 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2202.12654
machine learning\(k\)-meansvirtual element methodconvolutional neural networkspolyhedral discontinuous Galerkinpolyhedral grid refinement
Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Numerical methods for partial differential equations, boundary value problems (65Nxx) Elliptic equations and elliptic systems (35Jxx)
Related Items (6)
Accelerating algebraic multigrid methods via artificial neural networks ⋮ Quasi-optimal \textit{hp}-finite element refinements towards singularities via deep neural network prediction ⋮ Agglomeration of polygonal grids using graph neural networks with applications to multigrid solvers ⋮ Learning Robust Marking Policies for Adaptive Mesh Refinement ⋮ Learning adaptive coarse basis functions of FETI-DP ⋮ Adaptive discontinuous Galerkin finite element methods for the Allen-Cahn equation on polygonal meshes
Uses Software
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