Benchmark of Polygon Quality Metrics for Polytopal Element Methods
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Abstract (arXiv)
Polytopal Element Methods (PEM) allow to solve differential equations on general polygonal and polyhedral grids, potentially offering great flexibility to mesh generation algorithms. Differently from classical finite element methods, where the relation between the geometric properties of the mesh and the performances of the solver are well known, the characterization of a good polytopal element is still subject to ongoing research. Current shape regularity criteria are quite restrictive, and greatly limit the set of valid meshes. Nevertheless, numerical experiments revealed that PEM solvers can perform well on meshes that are far outside the strict boundaries imposed by the current theory, suggesting that the real capabilities of these methods are much higher. In this work, we propose a benchmark to study the correlation between general 2D polygonal meshes and PEM solvers. The benchmark aims to explore the space of 2D polygonal meshes and polygonal quality metrics, in order to identify weaker shape-regularity criteria under which the considered methods can reliably work. The proposed tool is quite general, and can be potentially used to study any PEM solver. Besides discussing the basics of the benchmark, in the second part of the paper we demonstrate its application on a representative member of the PEM family, namely the Virtual Element Method, also discussing our findings.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 May 2019.
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Citing papers per year: 2021: 1; 2022: 2.
3 papers.
- 2021Refinement of polygonal grids using Convolutional Neural Networks with applications to polygonal Discontinuous Galerkin and Virtual Element methodscited by 5
- 2022Agglomeration of Polygonal Grids using Graph Neural Networks with applications to Multigrid solverscited by 3
- 2022Machine Learning based refinement strategies for polyhedral grids with applications to Virtual Element and polyhedral Discontinuous Galerkin methodscited by 2
- 2022Agglomeration of Polygonal Grids using Graph Neural Networks with applications to Multigrid solvers31 Oct 2022
- 2022Machine Learning based refinement strategies for polyhedral grids with applications to Virtual Element and polyhedral Discontinuous Galerkin methods25 Feb 2022
- 2021Refinement of polygonal grids using Convolutional Neural Networks with applications to polygonal Discontinuous Galerkin and Virtual Element methods10 Feb 2021