The derivative based variance sensitivity analysis for the distribution parameters and its computation

Pan Wang, Zhenzhou Lu, Bo Ren and Lei Cheng

Reliability Engineering and System Safety, 2013, vol. 119, issue C, 305-315

Abstract: The output variance is an important measure for the performance of a structural system, and it is always influenced by the distribution parameters of inputs. In order to identify the influential distribution parameters and make it clear that how those distribution parameters influence the output variance, this work presents the derivative based variance sensitivity decomposition according to Sobol′s variance decomposition, and proposes the derivative based main and total sensitivity indices. By transforming the derivatives of various orders variance contributions into the form of expectation via kernel function, the proposed main and total sensitivity indices can be seen as the “by-product†of Sobol′s variance based sensitivity analysis without any additional output evaluation. Since Sobol′s variance based sensitivity indices have been computed efficiently by the sparse grid integration method, this work also employs the sparse grid integration method to compute the derivative based main and total sensitivity indices. Several examples are used to demonstrate the rationality of the proposed sensitivity indices and the accuracy of the applied method.

Keywords: Variance sensitivity decomposition; Derivative based sensitivity; Main and total sensitivity indices; Kernel function; Sparse grid integration (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:119:y:2013:i:c:p:305-315

DOI: 10.1016/j.ress.2013.07.003

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