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Gradient and parameter sensitivity estimation for systems evaluated using Monte Carlo analysis

Author

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  • Ahammed, Mukshed
  • Melchers, Robert E.

Abstract

The performance evaluation of many practical systems can be handled only through computationally intensive Monte Carlo simulation. Although a number of specialist techniques have been proposed, in general, estimation of the sensitivity of the outcome to changes in parameters involves duplicate simulations and finite differences for each parameter of interest. An approximate technique for gradient sensitivity estimation was outlined previously. It is appropriate when the performance function is uni-modal and relatively smooth in the region of interest. It generates all gradients simultaneously by converting Monte Carlo simulation run outcomes to an approximate analytic problem defined by a simplified response surface. The gradients then follow immediately. No extra simulation runs are required. Herein that approach is extended to non-Normal random variables and to the estimation of parameter sensitivities for random variable means and standard deviations. Some illustrative examples are given with comparisons to sensitivities computed by conventional Monte Carlo. The influence of constraint function(s) defining the admissible solution region is also considered.

Suggested Citation

  • Ahammed, Mukshed & Melchers, Robert E., 2006. "Gradient and parameter sensitivity estimation for systems evaluated using Monte Carlo analysis," Reliability Engineering and System Safety, Elsevier, vol. 91(5), pages 594-601.
    • Handle: RePEc:eee:reensy:v:91:y:2006:i:5:p:594-601
    DOI: 10.1016/j.ress.2005.04.005
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    Citations

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    Cited by:

    1. Zio, E. & Pedroni, N., 2012. "Monte Carlo simulation-based sensitivity analysis of the model of a thermal–hydraulic passive system," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 90-106.
    2. Song, Shufang & Lu, Zhenzhou & Qiao, Hongwei, 2009. "Subset simulation for structural reliability sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 658-665.
    3. Conceição António, Carlos & Hoffbauer, Luísa N., 2007. "Uncertainty analysis based on sensitivity applied to angle-ply composite structures," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1353-1362.
    4. Tianxiao Zhang & Yimin Zhang, 2017. "A new model for reliability design and reliability sensitivity analysis of a hydraulic piston pump," Journal of Risk and Reliability, , vol. 231(1), pages 11-24, February.
    5. Carlon, André Gustavo & Kroetz, Henrique Machado & Torii, André Jacomel & Lopez, Rafael Holdorf & Miguel, Leandro Fleck Fadel, 2022. "Risk optimization using the Chernoff bound and stochastic gradient descent," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    6. Zio, E. & Pedroni, N., 2009. "Functional failure analysis of a thermal–hydraulic passive system by means of Line Sampling," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1764-1781.
    7. Zio, E. & Pedroni, N., 2010. "An optimized Line Sampling method for the estimation of the failure probability of nuclear passive systems," Reliability Engineering and System Safety, Elsevier, vol. 95(12), pages 1300-1313.

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