 Sensitivity analysis via Karhunen–Loève expansion of a random field model: Estimation of Sobol’ indices and experimental designLuc Pronzato Reliability Engineering and System Safety, 2019, vol. 187, issue C, 93-109 Abstract: We use the Karhunen–Loève expansion of a random-field model to construct a tensorised Bayesian linear model from which Sobol’ sensitivity indices can be estimated straightforwardly. The method combines the advantages of models built from families of orthonormal functions, which facilitate computations, and Gaussian-process models, which offer a lot of flexibility. The posterior distribution of the indices can be derived, and its normal approximation can be used to design experiments especially adapted to their estimation. Implementation details are provided, and values of tuning parameters are indicated that yield precise estimation from a small number of function evaluations. Several illustrative examples are included that show the good performance of the method, in particular in comparison with estimation based on polynomial chaos expansion. Keywords: Sensitivity analysis; Sobol’ Indices; Random-field model; Karhunen–Loève expansion; Bayesian linear model; Polynomial chaos; Optimal design of experiments (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:187:y:2019:i:c:p:93-109 DOI: 10.1016/j.ress.2018.01.010 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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