Uncertainty quantification for stochastic dynamical systems using time-dependent stochastic bases

A novel method based on time-dependent stochastic orthogonal bases for stochastic response surface approximation is proposed to overcome the problem of significant errors in the utilization of the generalized polynomial chaos (GPC) method that approximates the stochastic response by orthogonal polyn...

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Bibliographic Details
Published in:Applied mathematics and mechanics 2019, Vol.40 (1), p.63-84
Main Authors: Lan, Jinchun, Zhang, Qianlong, Wei, Sha, Peng, Zhike, Dong, Xinjian, Zhang, Wenming
Format: Article
Language:English
Subjects:
Applications of Mathematics
Approximation
Classical Mechanics
Economic models
Fluid- and Aerodynamics
Mathematical analysis
Mathematical Modeling and Industrial Mathematics
Mathematics
Mathematics and Statistics
Partial Differential Equations
Polynomials
Probability density functions
Response surface methodology
Time dependence
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Summary:A novel method based on time-dependent stochastic orthogonal bases for stochastic response surface approximation is proposed to overcome the problem of significant errors in the utilization of the generalized polynomial chaos (GPC) method that approximates the stochastic response by orthogonal polynomials. The accuracy and effectiveness of the method are illustrated by different numerical examples including both linear and nonlinear problems. The results indicate that the proposed method modifies the stochastic bases adaptively, and has a better approximation for the probability density function in contrast to the GPC method.
ISSN:0253-4827
1573-2754
DOI:10.1007/s10483-019-2409-6