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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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    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.
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    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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