Asymptotic error distribution of numerical methods for parabolic SPDEs with multiplicative noise

TL;DR

This paper investigates the asymptotic error distribution of numerical methods for parabolic SPDEs with multiplicative noise, revealing that the error is governed by a linear SPDE driven by infinitely many Wiener processes.

Contribution

It provides the limit distribution of the normalized error for the exponential Euler method and explores how the error distribution depends on the problem, unlike additive noise cases.

Findings

Error distribution governed by a linear SPDE driven by infinitely many Wiener processes

Asymptotic error distribution derived for full discretization methods

Strong convergence speed varies significantly depending on the specific problem

Abstract

This paper aims to investigate the asymptotic error distribution of several numerical methods for stochastic partial differential equations (SPDEs) with multiplicative noise. Firstly, we give the limit distribution of the normalized error process of the exponential Euler method in $\dot{H}^\eta$ for some $\eta>0$. A key finding is that the asymptotic error in distribution of the exponential Euler method is governed by a linear SPDE driven by infinitely many independent $Q$-Wiener processes. This characteristic represents a significant difference from numerical methods for both stochastic ordinary differential equations and SPDEs with additive noise. Secondly, as applications of the above result, we derive the asymptotic error distribution of a full discretization based on the temporal exponential Euler method and the spatial finite element method. As a concrete illustration, we provide the pointwise limit distribution of the normalized error process when the exponential Euler method is applied to a specific class of stochastic heat equations. Finally, by studying the asymptotic error of the spatial semi-discrete spectral Galerkin method, we demonstrate that the actual strong convergence speed of spatial semi-discrete numerical methods may be highly problem-dependent, rather than universally predictable.