A High-Order Kernel Method for Diffusion and Reaction-Diffusion Equations on Surfaces

TL;DR

This paper introduces a high-order kernel method using radial basis functions for solving diffusion and reaction-diffusion PDEs on smooth surfaces, avoiding coordinate issues and demonstrating accuracy and stability through numerical experiments.

Contribution

The paper presents a novel RBF-based kernel method for PDEs on surfaces that does not rely on surface metrics or coordinate systems, enhancing robustness and applicability.

Findings

Accurate error estimates for surface derivative operators.

Numerical stability demonstrated through experiments.

Effective application to biological and chemical PDE systems.

Abstract

In this paper we present a high-order kernel method for numerically solving diffusion and reaction-diffusion partial differential equations (PDEs) on smooth, closed surfaces embedded in $\mathbb{R}^d$. For two-dimensional surfaces embedded in $\mathbb{R}^3$, these types of problems have received growing interest in biology, chemistry, and computer graphics to model such things as diffusion of chemicals on biological cells or membranes, pattern formations in biology, nonlinear chemical oscillators in excitable media, and texture mappings. Our kernel method is based on radial basis functions (RBFs) and uses a semi-discrete approach (or the method-of-lines) in which the surface derivative operators that appear in the PDEs are approximated using collocation. The method only requires nodes at "scattered" locations on the surface and the corresponding normal vectors to the surface. Additionally, it does not rely on any surface-based metrics and avoids any intrinsic coordinate systems, and thus does not suffer from any coordinate distortions or singularities. We provide error estimates for the kernel-based approximate surface derivative operators and numerically study the accuracy and stability of the method. Applications to different non-linear systems of PDEs that arise in biology and chemistry are also presented.