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Workshop

Potential (&) pitfalls in RBF-FD discretization of PDEs

  • Sabine Le Borne (TU Hamburg)
E1 05 (Leibniz-Saal)

Abstract

There exist several discretization techniques for the numerical solution of partial differential equations (PDEs). In addition to classical finite difference, finite element and finite volume techniques, a more recent approach employs radial basis functions to generate differentiation stencils on unstructured point sets. This approach, abbreviated by RBF-FD (radial basis function - finite difference), has gained in popularity since it enjoys several advantages: It is (relatively) straightforward, does not require a mesh and generalizes easily to higher spatial dimensions. However, its application is not quite as blackbox as it may appear at first sight. The computed solution might suffer severely from various sources of errors if RBF-FD parameters are not selected carefully.

Through comprehensive numerical experiments, we study the influence of several of these parameters on the condition numbers of intermediate (local) weight matrices, on the condition number of the resulting (global) stiffness matrix and ultimately on the approximation error of the computed discrete solution to the partial differential equation. The parameters of investigation include the type of RBF (and its shape or other parameters if applicable), the degree of polynomial augmentation, the discretization stencil size, the underlying type of point set (structured/unstructured), and the total number of (interior and boundary) points to discretize the PDE, here chosen as a three-dimensional Poisson's problem with Dirichlet boundary conditions.

The goal is to illustrate and steer away from potential pitfalls in RBF-FD discretization where a computationally more expensive set-up not always leads to a more accurate numerical solution, and to guide toward a compatible selection of RBF-FD parameters.

Katja Heid

Max Planck Institute for Mathematics in the Sciences, Leipzig Contact via Mail

Peter Benner

Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg

Lars Grasedyck

RWTH Aachen

André Uschmajew

Max Planck Institute for Mathematics in the Sciences, Leipzig