An RBF-FD closest point method for solving PDEs on surfaces

A. Petras*, Leevan LING, S. J. Ruuth

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

40 Citations (Scopus)


Partial differential equations (PDEs) on surfaces appear in many applications throughout the natural and applied sciences. The classical closest point method (Ruuth and Merriman (2008) [17]) is an embedding method for solving PDEs on surfaces using standard finite difference schemes. In this paper, we formulate an explicit closest point method using finite difference schemes derived from radial basis functions (RBF-FD). Unlike the orthogonal gradients method (Piret (2012) [22]), our proposed method uses RBF centers on regular grid nodes. This formulation not only reduces the computational cost but also avoids the ill-conditioning from point clustering on the surface and is more natural to couple with a grid based manifold evolution algorithm (Leung and Zhao (2009) [26]). When compared to the standard finite difference discretization of the closest point method, the proposed method requires a smaller computational domain surrounding the surface, resulting in a decrease in the number of sampling points on the surface. In addition, higher-order schemes can easily be constructed by increasing the number of points in the RBF-FD stencil. Applications to a variety of examples are provided to illustrate the numerical convergence of the method.

Original languageEnglish
Pages (from-to)43-57
Number of pages15
JournalJournal of Computational Physics
Publication statusPublished - 1 Oct 2018

Scopus Subject Areas

  • Numerical Analysis
  • Modelling and Simulation
  • Physics and Astronomy (miscellaneous)
  • Physics and Astronomy(all)
  • Computer Science Applications
  • Computational Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Closest point method
  • Embedding method
  • Finite differences
  • Radial basis functions


Dive into the research topics of 'An RBF-FD closest point method for solving PDEs on surfaces'. Together they form a unique fingerprint.

Cite this