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Data-based stochastic model reduction for the Kuramoto–Sivashinsky equation

The problem of constructing data-based, predictive, reduced models for the Kuramoto–Sivashinsky equation is considered, under circumstances where one has observation data only for a small subset of the dynamical variables. Accurate prediction is achieved by developing a discrete-time stochastic reduced system, based on a NARMAX (Nonlinear Autoregressive Moving Average with eXogenous input) representation. The practical issue, with the NARMAX representation as with any other, is to identify an efficient structure, i.e., one with a small number of terms and coefficients. This is accomplished here by estimating coefficients for an approximate inertial form. The broader significance of the results is discussed.

Identiferoai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/622792
Date01 February 2017
CreatorsLu, Fei, Lin, Kevin K., Chorin, Alexandre J.
ContributorsSchool of Mathematics, University of Arizona
PublisherELSEVIER SCI LTD
Source SetsUniversity of Arizona
LanguageEnglish
Detected LanguageEnglish
TypeArticle
Rights© 2016 Elsevier B.V. All rights reserved.
Relationhttp://linkinghub.elsevier.com/retrieve/pii/S0167278915301652

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