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Extremum sensitivity analysis with polynomial Monte Carlo filtering

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  • Wong, Chun Yui
  • Seshadri, Pranay
  • Parks, Geoffrey

Abstract

Global sensitivity analysis is a powerful set of ideas and heuristics for understanding the importance and interplay between uncertain parameters in a computational model. Such a model is characterized by a set of input parameters and an output quantity of interest, where we typically assume that the inputs are independent and their marginal densities are known. If the output quantity is smooth, polynomial chaos can be used to extract Sobol’ indices.

Suggested Citation

  • Wong, Chun Yui & Seshadri, Pranay & Parks, Geoffrey, 2021. "Extremum sensitivity analysis with polynomial Monte Carlo filtering," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    • Handle: RePEc:eee:reensy:v:212:y:2021:i:c:s095183202100154x
    DOI: 10.1016/j.ress.2021.107609
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    References listed on IDEAS

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    1. Blatman, Géraud & Sudret, Bruno, 2010. "Efficient computation of global sensitivity indices using sparse polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 95(11), pages 1216-1229.
    2. Liu, Ruixue & Owen, Art B., 2006. "Estimating Mean Dimensionality of Analysis of Variance Decompositions," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 712-721, June.
    3. Yingcun Xia & Howell Tong & W. K. Li & Li‐Xing Zhu, 2002. "An adaptive estimation of dimension reduction space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 363-410, August.
    4. Sobol′ , I.M, 2001. "Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 55(1), pages 271-280.
    5. Sudret, B. & Mai, C.V., 2015. "Computing derivative-based global sensitivity measures using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 241-250.
    6. Constantine, Paul G. & Diaz, Paul, 2017. "Global sensitivity metrics from active subspaces," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 1-13.
    7. Sudret, Bruno, 2008. "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 964-979.
    8. Sobol’, I.M. & Kucherenko, S., 2009. "Derivative based global sensitivity measures and their link with global sensitivity indices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(10), pages 3009-3017.
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    Cited by:

    1. Shang, Yue & Nogal, Maria & Teixeira, Rui & Wolfert, A.R. (Rogier) M., 2024. "Extreme-oriented sensitivity analysis using sparse polynomial chaos expansion. Application to train–track–bridge systems," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

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