Representation of Manifolds for the Stochastic Swift-Hohenberg Equation with Multiplicative Noise

Representation of approximation for manifolds of the stochastic Swift-Hohenberg equation with multiplicative noise has been investigated via non-Markovian reduced system. The approximate parameterizations of the small scales for the large scales are given in the process of seeking for stochastic par...

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Main Authors: Yanfeng Guo, Donglong Li
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Advances in Mathematical Physics
Online Access:http://dx.doi.org/10.1155/2020/2676919
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author Yanfeng Guo
Donglong Li
author_facet Yanfeng Guo
Donglong Li
author_sort Yanfeng Guo
collection DOAJ
description Representation of approximation for manifolds of the stochastic Swift-Hohenberg equation with multiplicative noise has been investigated via non-Markovian reduced system. The approximate parameterizations of the small scales for the large scales are given in the process of seeking for stochastic parameterizing manifolds, which are obtained as pullback limits of some backward-forward systems depending on the time-history of the dynamics of the low modes in a mean square sense through the nonlinear terms. When the corresponding pullback limits of some backward-forward systems are efficiently determined, the corresponding non-Markovian reduced systems can be obtained for researching good modeling performances in practice.
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id doaj-art-95b9d62bcd6642558f7c4b7e2c69d8bc
institution Kabale University
issn 1687-9120
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publishDate 2020-01-01
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spelling doaj-art-95b9d62bcd6642558f7c4b7e2c69d8bc2025-02-03T01:30:31ZengWileyAdvances in Mathematical Physics1687-91201687-91392020-01-01202010.1155/2020/26769192676919Representation of Manifolds for the Stochastic Swift-Hohenberg Equation with Multiplicative NoiseYanfeng Guo0Donglong Li1School of Science, Guangxi University of Science and Technology, Liuzhou, Guangxi, 545006, ChinaSchool of Science, Guangxi University of Science and Technology, Liuzhou, Guangxi, 545006, ChinaRepresentation of approximation for manifolds of the stochastic Swift-Hohenberg equation with multiplicative noise has been investigated via non-Markovian reduced system. The approximate parameterizations of the small scales for the large scales are given in the process of seeking for stochastic parameterizing manifolds, which are obtained as pullback limits of some backward-forward systems depending on the time-history of the dynamics of the low modes in a mean square sense through the nonlinear terms. When the corresponding pullback limits of some backward-forward systems are efficiently determined, the corresponding non-Markovian reduced systems can be obtained for researching good modeling performances in practice.http://dx.doi.org/10.1155/2020/2676919
spellingShingle Yanfeng Guo
Donglong Li
Representation of Manifolds for the Stochastic Swift-Hohenberg Equation with Multiplicative Noise
Advances in Mathematical Physics
title Representation of Manifolds for the Stochastic Swift-Hohenberg Equation with Multiplicative Noise
title_full Representation of Manifolds for the Stochastic Swift-Hohenberg Equation with Multiplicative Noise
title_fullStr Representation of Manifolds for the Stochastic Swift-Hohenberg Equation with Multiplicative Noise
title_full_unstemmed Representation of Manifolds for the Stochastic Swift-Hohenberg Equation with Multiplicative Noise
title_short Representation of Manifolds for the Stochastic Swift-Hohenberg Equation with Multiplicative Noise
title_sort representation of manifolds for the stochastic swift hohenberg equation with multiplicative noise
url http://dx.doi.org/10.1155/2020/2676919
work_keys_str_mv AT yanfengguo representationofmanifoldsforthestochasticswifthohenbergequationwithmultiplicativenoise
AT donglongli representationofmanifoldsforthestochasticswifthohenbergequationwithmultiplicativenoise