Non-intrusive stochastic analysis with parameterized imprecise probability models: I. Performance estimation

Pengfei Wei, Jingwen Song, Sifeng Bi, Matteo Broggi, Michael Beer, Zhenzhou Lu, Zhufeng Yue

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)349-368
Number of pages20
JournalMechanical Systems and Signal Processing
Volume124
Early online date8 Feb 2019
DOIs
Publication statusPublished - 1 Jun 2019

Keywords

  • aleatory and epistemic uncertainties
  • high-dimensional model representation
  • imprecise probability models
  • imprecise stochastic simulation
  • sensitivity analysis
  • uncertainty quantification

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