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Global sensitivity analysis for multivariate output model and dynamic models

Author

Listed:
  • Zhang, Kaichao
  • Lu, Zhenzhou
  • Cheng, Kai
  • Wang, Laijun
  • Guo, Yanling

Abstract

Global sensitivity analysis has mainly been analyzed for scalar output and static models, though many mathematical and computational models used in engineering produce multivariate output that show some degree of correlation, and most physical systems are dynamic models. This paper focuses on global sensitivity analysis for multivariate output and dynamic models and a novel procedure is proposed to research the influence of inputs and model modes on the synthetic uncertainty of output. Introducing an additional variable to represent the variation of model modes which is viewed as model framework uncertainty, the variance decompositions of multivariate output and dynamic models are obtained and the significance of variance contributions is presented in detail. Two numerical examples and two practical models are employed to illustrate the validity and usefulness of the novel global sensitivity analysis approach.

Suggested Citation

  • Zhang, Kaichao & Lu, Zhenzhou & Cheng, Kai & Wang, Laijun & Guo, Yanling, 2020. "Global sensitivity analysis for multivariate output model and dynamic models," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:reensy:v:204:y:2020:i:c:s0951832020306967
    DOI: 10.1016/j.ress.2020.107195
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    References listed on IDEAS

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    1. Sinan Xiao & Zhenzhou Lu & Pan Wang, 2018. "Multivariate Global Sensitivity Analysis Based on Distance Components Decomposition," Risk Analysis, John Wiley & Sons, vol. 38(12), pages 2703-2721, December.
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    7. Ehre, Max & Papaioannou, Iason & Straub, Daniel, 2020. "Global sensitivity analysis in high dimensions with PLS-PCE," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
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    Cited by:

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    2. Torii, André Jacomel & Novotny, Antonio André, 2021. "A priori error estimates for local reliability-based sensitivity analysis with Monte Carlo Simulation," Reliability Engineering and System Safety, Elsevier, vol. 213(C).

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