Non-intrusive stochastic analysis with parameterized imprecise probability models: I. Performance estimation. (1st June 2019)

• Record Type: Journal Article
• Authors: Wei, Pengfei; Song, Jingwen; Bi, Sifeng; Broggi, Matteo; Beer, Michael; Lu, Zhenzhou; Yue, Zhufeng
• Is Part Of: Mechanical systems and signal processing. Volume 124(2019)
• Page Start: 349
• Page End: 368
• Publication Date: 2019-06-01
• Subjects: Imprecise stochastic simulation -- Uncertainty quantification -- Imprecise probability models -- High-dimensional model representation -- Sensitivity analysis -- Aleatory and epistemic uncertainties; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2019.01.058
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Imprecise stochastic simulation framework is developed. Both local and global methods are developed. The methods are quite effective for propagations of imprecise probability models. The truncation errors are assessed by sensitivity indices. The statistical errors are evaluated by standard deviations of estimators. Abstract: Uncertainty propagation through the simulation models is critical for computational mechanics engineering to provide robust and reliable design in the presence of polymorphic uncertainty. This set of companion papers present a general framework, termed as non-intrusive imprecise stochastic simulation, for uncertainty propagation under the background of imprecise probability. This framework is composed of a set of methods developed for meeting different goals. In this paper, the performance estimation is concerned. The local extended Monte Carlo simulation (EMCS) is firstly reviewed, and then the global EMCS is devised to improve the global performance. Secondly, the cut-HDMR (High-Dimensional Model Representation) is introduced for decomposing the probabilistic response functions, and the local EMCS method is used for estimating the cut-HDMR component functions. Thirdly, the RS (Random Sampling)-HDMR is introduced to decompose the probabilistic response functions, and the global EMCS is applied for estimating the RS-HDMR component functions. The statistical errors of all estimators are derived, and the truncation errors are estimated by two global sensitivity indices, which can also be used for identifying the influential HDMR components. In the companion paper, the reliability and rare event analysis are treated. The effectiveness of the proposed methods are demonstrated by numerical and engineering examples.