The Wong-Zakai approximations of invariant manifolds for stochastic evolution equations with linear noise

In this paper, we study the Wong-Zakai approximations given by a class of stationary processes and their associated dynamics of a class of stochastic evolution equations with linear noise. We first prove that the solutions of Wong-Zakai approximations converge almost surely to the solutions of the stochastic evolution equation, with a polynomial rate. Then we prove the existence and smoothness of invariant manifolds for the stochastic evolution equation and its Wong-Zakai approximation equations. We also show that the invariant manifolds of the Wong-Zakai approximations converge almost surely to the invariant manifolds of the stochastic evolution equation, with a polynomial rate.

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Publication Details

Journal
Communications on Pure &amp Applied Analysis
Published
2025-01-01
DOI
https://doi.org/10.3934/cpaa.2025068
Primary Topic
Stochastic processes and financial applications
Type
article
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article

The Wong-Zakai approximations of invariant manifolds for stochastic evolution equations with linear noise

Shao Hui-cheng
Communications on Pure &amp Applied Analysis
Stochastic processes and financial applications
article

The Wong-Zakai approximations of invariant manifolds for stochastic evolution equations with linear noise

Shao Hui-cheng
article en

Abstract

In this paper, we study the Wong-Zakai approximations given by a class of stationary processes and their associated dynamics of a class of stochastic evolution equations with linear noise. We first prove that the solutions of Wong-Zakai approximations converge almost surely to the solutions of the stochastic evolution equation, with a polynomial rate. Then we prove the existence and smoothness of invariant manifolds for the stochastic evolution equation and its Wong-Zakai approximation equations. We also show that the invariant manifolds of the Wong-Zakai approximations converge almost surely to the invariant manifolds of the stochastic evolution equation, with a polynomial rate.

Communications on Pure &amp Applied AnalysisVol. 24(10)
Openalex Percentile: Top 86%
Stochastic processes and financial applications
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