Kernel-based Methods for Stochastic Partial Differential Equations

Abstract

This article gives a new insight of kernel-based (approximation) methods to solve the high-dimensional stochastic partial differential equations. We will combine the techniques of meshfree approximation and kriging interpolation to extend the kernel-based methods for the deterministic data to the stochastic data. The main idea is to endow the Sobolev spaces with the probability measures induced by the positive definite kernels such that the Gaussian random variables can be well-defined on the Sobolev spaces. The constructions of these Gaussian random variables provide the kernel-based approximate solutions of the stochastic models. In the numerical examples of the stochastic Poisson and heat equations, we show that the approximate probability distributions are well-posed for various kinds of kernels such as the compactly supported kernels (Wendland functions) and the Sobolev-spline kernels (Matérn functions).