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

PHYSICA D-NONLINEAR PHENOMENA

卷:340

Article

Lu, Fei^1,2  Lin, Kevin K.³  Chorin, Alexandre J.^1,2 

[1] Univ Calif Berkeley, Dept Math, Berkeley, CA 94720 USA

[2] Lawrence Berkeley Natl Lab, Berkeley, CA 94720 USA

[3] Univ Arizona, Sch Math, Tucson, AZ 85721 USA

关键词: Stochastic parametrization;    NARMAX;    Kuramoto-Sivashinsky equation;    Approximate inertial manifold;   

DOI  :  10.1016/j.physd.2016.09.007

来源: Elsevier

【 摘 要 】

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. (C) 2016 Elsevier B.V. All rights reserved.

【 授权许可】

Free