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The Galerkin Analysis for the Random Periodic Solution of Semilinear Stochastic Evolution Equations

Yue Wu Orcid Logo, Chenggui Yuan Orcid Logo

Journal of Theoretical Probability, Volume: 36, Issue: 1

Swansea University Author: Chenggui Yuan Orcid Logo

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DOI (Published version): 10.1007/s10959-023-01236-x

Abstract

In this paper, we study the numerical method for approximating the random periodic solution of semilinear 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 uniq...

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Published in: Journal of Theoretical Probability
ISSN: 0894-9840 1572-9230
Published: Springer Science and Business Media LLC 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa62486
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Abstract: In this paper, we study the numerical method for approximating the random periodic solution of semilinear 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 a best order of convergence that is arbitrarily close to 0.5.
Keywords: Random periodic solution; Stochastic evolution equations; Galerkin method; Discrete exponential integrator scheme
College: Faculty of Science and Engineering
Funders: This work is supported by the Alan Turing Institute for funding this work under EPSRC grant EP/N510129/1 and EPSRC for funding though the project EP/S026347/1, titled ‘Unparameterised multi-modal data, high order signatures, and the mathematics of data science’.
Issue: 1

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