Global sensitivity analysis in high dimensions with partial least squares-driven PCEs

Max Ehre, Iason Papaioannou, Daniel Straub

Research output: Contribution to conferencePaperpeer-review

Abstract

We develop an efficient method for the computation of variance-based sensitivity indices using a recently introduced latent-variable-based polynomial chaos expansion, which is particularly suitable for high dimensional problems. By back-transforming the surrogate from its latent variable space-basis to the original input variable space-basis, we derive analytical expressions for these sensitivities that only depend on the model coefficients. Thus, once the surrogate model is built, the variance-based sensitivities can be computed at negligible computational cost as no additional sampling is required. The accuracy of the method is demonstrated with a numerical experiment of an elastic truss.

Original languageEnglish
StatePublished - 2019
Event13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019 - Seoul, Korea, Republic of
Duration: 26 May 201930 May 2019

Conference

Conference13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019
Country/TerritoryKorea, Republic of
CitySeoul
Period26/05/1930/05/19

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