Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
175 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Mesh Optimization for the Virtual Element Method: How Small Can an Agglomerated Mesh Become? (2404.11484v1)

Published 17 Apr 2024 in math.NA and cs.NA

Abstract: We present an optimization procedure for generic polygonal or polyhedral meshes, tailored for the Virtual Element Method (VEM). Once the local quality of the mesh elements is analyzed through a quality indicator specific to the VEM, groups of elements are agglomerated to optimize the global mesh quality. The resulting discretization is significantly lighter: we can remove up to 80$\%$ of the mesh elements, based on a user-set parameter, thus reducing the number of faces, edges, and vertices. This results in a drastic reduction of the total number of degrees of freedom associated with a discrete problem defined over the mesh with the VEM, in particular, for high-order formulations. We show how the VEM convergence rate is preserved in the optimized meshes, and the approximation errors are comparable with those obtained with the original ones. We observe that the optimization has a regularization effect over low-quality meshes, removing the most pathological elements. This regularization effect is evident in cases where the original meshes cause the VEM to diverge, while the optimized meshes lead to convergence. We conclude by showing how the optimization of a real CAD model can be used effectively in the simulation of a time-dependent problem.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (32)
  1. DSH Lo. Finite element mesh generation. CRC Press, New York, 2014.
  2. A survey of indicators for mesh quality assessment. In Computer Graphics Forum, volume 42-2, pages 461–483. Wiley Online Library, 2023.
  3. P Knupp. Algebraic mesh quality metrics. SIAM Journal on Scientific Computing, 23(1):193–218, 2001.
  4. The Verdict library reference manual. Sandia National Laboratories Technical Report, 9(6), 2007.
  5. Measuring regularity of convex polygons. Computer-Aided Design, 45(2):93–104, 2013.
  6. J Zunic and PL Rosin. A new convexity measure for polygons. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(7):923–934, 2004.
  7. W Huang and Y Wang. Anisotropic mesh quality measures and adaptation for polygonal meshes. Journal of Computational Physics, 410:109368, 2020.
  8. The role of mesh quality and mesh quality indicators in the virtual element method. Advances in Computational Mathematics, 48(1):3, 2021.
  9. Polyhedral mesh quality indicator for the virtual element method. Computers & Mathematics with Applications, 114:151–160, 2022.
  10. S Berrone and A D’Auria. A new quality preserving polygonal mesh refinement algorithm for polygonal element methods. Finite Elements in Analysis and Design, 207:103770, 2022.
  11. Basic principles of virtual element methods. Mathematical Models and Methods in Applied Sciences, 23(01):199–214, 2013.
  12. Discontinuous Galerkin approximations for elliptic problems. Numerical Methods for Partial Differential Equations, 16(4):365–378, 2000.
  13. The mimetic finite difference method, volume 11 of Modeling, Simulations and Applications. Springer, San Diego, CA, USA, I edition, 2014.
  14. Conforming polygonal finite elements. International Journal for Numerical Methods in Engineering, 61(12):2045–2066, 2004.
  15. The hybrid high-order method for polytopal meshes. Number 19 in Modeling, Simulation and Application, 2020.
  16. A cell-centered second-order accurate finite volume method for convection–diffusion problems on unstructured meshes. Mathematical Models and Methods in Applied Sciences, 14(08):1235–1260, 2004.
  17. The hitchhiker’s guide to the virtual element method. Mathematical Models and Methods in Applied Sciences, 24(8):1541–1573, 2014.
  18. Efficient computation of clipped voronoi diagram for mesh generation. Computer-Aided Design, 45(4):843–852, 2013.
  19. Paola F Antonietti and E Manuzzi. Refinement of polygonal grids using convolutional neural networks with applications to polygonal discontinuous galerkin and virtual element methods. Journal of Computational Physics, 452:110900, 2022.
  20. Agglomeration-based geometric multigrid schemes for the virtual element method. SIAM Journal on Numerical Analysis, 61(1):223–249, 2023.
  21. Mesh quality agglomeration algorithm for the virtual element method applied to discrete fracture networks. Calcolo, 60(2):27, 2023.
  22. Sobolev spaces. Pure and Applied Mathematics. Academic Press, Amsterdam, 2 edition, 2003.
  23. L Ridgway Scott and SC Brenner. The mathematical theory of finite element methods. Texts in Applied Mathematics 15. Springer-Verlag, New York, 3 edition, 2008.
  24. Equivalent projectors for virtual element methods. Computers & Mathematics with Applications, 66(3):376–391, 2013.
  25. Virtual element method for general second-order elliptic problems on polygonal meshes. Mathematical Models and Methods in Applied Sciences, 26(4):729–750, 2016.
  26. Marco Livesu. cinolib: a generic programming header only c++ library for processing polygonal and polyhedral meshes. Transactions on Computational Science XXXIV, pages 64–76, 2019.
  27. Metis: A software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices. University of Minnesota Digital Conservancy, 1997.
  28. Jonathan Richard Shewchuk. Triangle: Engineering a 2d quality mesh generator and delaunay triangulator. In Workshop on applied computational geometry, pages 203–222. Springer, 1996.
  29. Hang Si and A TetGen. A quality tetrahedral mesh generator and three-dimensional delaunay triangulator. Weierstrass Institute for Applied Analysis and Stochastic, Berlin, Germany, 81:12, 2006.
  30. Fast tetrahedral meshing in the wild. ACM Transactions on Graphics (TOG), 39(4):117–1, 2020.
  31. Parmetis: Parallel graph partitioning and sparse matrix ordering library. University of Minnesota Digital Conservancy, 1997.
  32. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11):1222–1239, 2001.

Summary

We haven't generated a summary for this paper yet.

X Twitter Logo Streamline Icon: https://streamlinehq.com