Averaging principle for semilinear stochastic partial differential equations involving space–time white noise

Hongge Yue; Yong Xu; Zhe Jiao

doi:10.1016/j.aml.2023.108686

Averaging principle for semilinear stochastic partial differential equations involving space–time white noise

Hongge Yue, Yong Xu, Zhe Jiao

School of Mathematics and Statistics

Northwestern Polytechnical University Xian

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

We study the averaging principle for a class of semilinear stochastic partial differential equations perturbed by space–time white noise. Using the factorization method and Burkholder's inequality, the estimation of stochastic integral involving the heat kernel is obtained. Under suitable assumptions, we show that the original stochastic systems can be approximated by the averaged equations.

Original language	English
Article number	108686
Journal	Applied Mathematics Letters
Volume	143
DOIs	https://doi.org/10.1016/j.aml.2023.108686
State	Published - Sep 2023

Keywords

Averaging principle
Semilinear stochastic partial differential equations
Space–time white noise

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10.1016/j.aml.2023.108686

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title = "Averaging principle for semilinear stochastic partial differential equations involving space–time white noise",

abstract = "We study the averaging principle for a class of semilinear stochastic partial differential equations perturbed by space–time white noise. Using the factorization method and Burkholder's inequality, the estimation of stochastic integral involving the heat kernel is obtained. Under suitable assumptions, we show that the original stochastic systems can be approximated by the averaged equations.",

keywords = "Averaging principle, Semilinear stochastic partial differential equations, Space–time white noise",

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year = "2023",

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AU - Yue, Hongge

AU - Xu, Yong

AU - Jiao, Zhe

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N2 - We study the averaging principle for a class of semilinear stochastic partial differential equations perturbed by space–time white noise. Using the factorization method and Burkholder's inequality, the estimation of stochastic integral involving the heat kernel is obtained. Under suitable assumptions, we show that the original stochastic systems can be approximated by the averaged equations.

AB - We study the averaging principle for a class of semilinear stochastic partial differential equations perturbed by space–time white noise. Using the factorization method and Burkholder's inequality, the estimation of stochastic integral involving the heat kernel is obtained. Under suitable assumptions, we show that the original stochastic systems can be approximated by the averaged equations.

KW - Averaging principle

KW - Semilinear stochastic partial differential equations

KW - Space–time white noise

UR - http://www.scopus.com/inward/record.url?scp=85152489427&partnerID=8YFLogxK

U2 - 10.1016/j.aml.2023.108686

DO - 10.1016/j.aml.2023.108686

M3 - 文章

AN - SCOPUS:85152489427

SN - 0893-9659

VL - 143

JO - Applied Mathematics Letters

JF - Applied Mathematics Letters

M1 - 108686

ER -

Averaging principle for semilinear stochastic partial differential equations involving space–time white noise

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Keywords

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