4.7 Article

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

Journal

MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 124, Issue -, Pages 349-368

Publisher

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2019.01.058

Keywords

Imprecise stochastic simulation; Uncertainty quantification; Imprecise probability models; High-dimensional model representation; Sensitivity analysis; Aleatory and epistemic uncertainties

Funding

  1. Natural Science Basic Research Plan in Shaanxi Province of China [2017JQ1007]
  2. Aerospace Science and Technology Foundation of China
  3. Top International University Visiting Program for Outstanding Young scholars of Northwestern Polytechnical University
  4. Alexander von Humboldt Foundation of Germany
  5. China Scholarships Council (CSC)
  6. National Natural Science Foundation of China [U1530122]

Ask authors/readers for more resources

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. (C) 2019 Elsevier Ltd. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available