What is it about?

This paper focuses on multivariate global sensitivity analysis of model output. The method proposed in this paper can help researchers find the important input variables which have significant effect on the model output.

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Why is it important?

Sensitivity analysis can help researchers understand how uncertainty in the output of a model (numerical or otherwise) can be apportioned to different sources of uncertainty in the model input. Thus, researchers can reduce the uncertainty of output effectively through allocating more resources (people, time, financial budget, etc.) to the most influential input variables and simplify the model by fixing the non‐influential input variables at the nominal values.

Perspectives

The method proposed in this paper can measure the effects of input variables on the correlation among different model outputs, which is missed in the previous studies.

Sinan Xiao

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This page is a summary of: Multivariate sensitivity analysis based on the direction of eigen space through principal component analysis, Reliability Engineering & System Safety, September 2017, Elsevier,
DOI: 10.1016/j.ress.2017.03.011.
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