 Polynomial chaos expansion for sensitivity analysis of model output with dependent inputsThierry A. Mara and William E. Becker Reliability Engineering and System Safety, 2021, vol. 214, issue C Abstract: In this paper, we discuss the sensitivity analysis of model response when the uncertain model inputs are not independent of one other. In this case, two different kinds of sensitivity indices can be evaluated: (i) the sensitivity indices that account for the dependence/correlation of an input or group of inputs with the remainder and (ii) the sensitivity indices that do not account for this dependence. We argue that this distinction applies to any global sensitivity measure. In the present work, we focus on the estimation of variance-based sensitivity indices which are based on the second-order moment of the model response of interest. In particular, we derive new strategies and new computationally efficient methods to assess them, which rely on the polynomial chaos expansion. Several numerical exercises are carried out to demonstrate the performance of the new methods, including a sensitivity analysis of a drainage model posterior to its statistical calibration. Keywords: Rosenblatt transformation; Nataf transformation; Mara–Tarantola transformation; Polynomial chaos expansion; Statistical calibration; Posterior sensitivity analysis (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:214:y:2021:i:c:s0951832021003185 DOI: 10.1016/j.ress.2021.107795 Access Statistics for this article Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares More articles in Reliability Engineering and System Safety from Elsevier |
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