The Galerkin analysis for the random periodic solution of semilinear stochastic evolution equations

TL;DR

This paper develops a Galerkin-based numerical method to approximate the random periodic solutions of semilinear stochastic evolution equations, proving convergence and analyzing the error rate in infinite-dimensional settings.

Contribution

It introduces a Galerkin-type exponential integrator scheme for these equations and establishes its convergence rate, which approaches 0.5, in the context of infinite-dimensional stochastic systems.

Findings

Existence and uniqueness of the random periodic solution.

Convergence rate of the proposed scheme approaches 0.5.

The scheme's strong error depends on the initial space.

Abstract

In this paper we study the numerical method for approximating the random periodic solution of semiliear stochastic evolution equations. The main challenge lies in proving a convergence over an infinite time horizon while simulating infinite-dimensional objects. We first show the existence and uniqueness of the random periodic solution to the equation as the limit of the pull-back flows of the equation, and observe that its mild form is well-defined in the intersection of a family of decreasing Hilbert spaces. Then we propose a Galerkin-type exponential integrator scheme and establish its convergence rate of the strong error to the mild solution, where the order of convergence directly depends on the space (among the family of Hilbert spaces) for the initial point to live. We finally conclude with the best order of convergence that is arbitrarily close to 0.5.