Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
129 tokens/sec
GPT-4o
28 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

A superconvergence result in the RBF-FD method (2404.03393v1)

Published 4 Apr 2024 in math.NA and cs.NA

Abstract: Radial Basis Function-generated Finite Differences (RBF-FD) is a meshless method that can be used to numerically solve partial differential equations. The solution procedure consists of two steps. First, the differential operator is discretised on given scattered nodes and afterwards, a global sparse matrix is assembled and inverted to obtain an approximate solution. Focusing on Polyharmonic Splines as our Radial Basis Functions (RBFs) of choice, appropriately augmented with monomials, it is well known that the truncation error of the differential operator approximation is determined by the degree of monomial augmentation. Naively, one might think that the solution error will have the same order of convergence. We present a superconvergence result that shows otherwise - for some augmentation degrees, order of convergence is higher than expected.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (15)
  1. Superconvergence of the shortley–weller approximation for dirichlet problems. Journal of Computational and Applied Mathematics, 116(2):263–273, 2000.
  2. V. Bayona. An insight into rbf-fd approximations augmented with polynomials. Computers & Mathematics with Applications, 77(9):2337–2353, 2019.
  3. On the role of polynomials in rbf-fd approximations: Ii. numerical solution of elliptic pdes. Journal of Computational Physics, 332:257–273, 2017.
  4. Parallel domain discretization algorithm for rbf-fd and other meshless numerical methods for solving pdes. Computers & Structures, 264:106773, 2022.
  5. G. E. Fasshauer. Meshfree Approximation Methods with Matlab. World SCientific, 2007.
  6. On the role of polynomials in rbf-fd approximations: I. interpolation and accuracy. Journal of Computational Physics, 321:21–38, 2016.
  7. M. Jančič and G. Kosec. Strong form mesh-free hp-adaptive solution of linear elasticity problem. Engineering with Computers, 2023.
  8. S. Le Borne and W. Leinen. Guidelines for rbf-fd discretization: Numerical experiments on the interplay of a multitude of parameter choices. Journal of Scientific Computing, 95(1):8, 2023.
  9. H. Li and X. Zhang. Superconvergence of high order finite difference schemes based on variational formulation for elliptic equations. Journal of Scientific Computing, 82, 02 2020.
  10. Meshless methods: A review and computer implementation aspects. Mathematics and Computers in Simulation, 79(3):763–813, 2008.
  11. J. Z. O. C. Zienkiewicz, R. L. Taylor. The finite element method: its basis and fundamentals. Butterworth-Heinemann, 6 edition, 2005.
  12. J. Slak and G. Kosec. Medusa: A C++ Library for Solving PDEs Using Strong Form Mesh-free Methods. ACM Transactions on Mathematical Software, 47(3):1–25, Sept. 2021.
  13. J. Strikwerda. Finite difference schemes and partial differential equations. Society for Industrial and Applied Mathematics, 2nd ed edition, 2004.
  14. A. Tolstykh and D. Shirobokov. On using radial basis functions in a “finite difference mode” with applications to elasticity problems. Computational Mechanics, 33:68–79, 12 2003.
  15. T. F. E. Wahlbin L. B., Dold A. (Ed). Superconvergence in Galerkin Finite Element Methods. 1995.

Summary

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