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Adaptive use of replicated Latin Hypercube Designs for computing Sobol’ sensitivity indices

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  • Damblin, Guillaume
  • Ghione, Alberto

Abstract

As recently pointed out in the field of Global Sensitivity Analysis (GSA) of computer simulations, the use of replicated Latin Hypercube Designs (rLHDs) is a cost-saving alternative to regular Monte Carlo sampling to estimate first-order Sobol’ indices. Indeed, two rLHDs are sufficient to compute the whole set of those indices regardless of the number of input variables. This relies on a permutation trick which, however, only works within the class of estimators called Oracle 2. In the present paper, we show that rLHDs are still beneficial to another class of estimators, called Oracle 1, which often outperforms Oracle 2 for estimating small and moderate indices. Even though unlike Oracle 2 the computation cost of Oracle 1 depends on the input dimension, the permutation trick can be applied to construct an averaged (triple) Oracle 1 estimator whose great accuracy is presented on a numerical example.

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  • Damblin, Guillaume & Ghione, Alberto, 2021. "Adaptive use of replicated Latin Hypercube Designs for computing Sobol’ sensitivity indices," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    • Handle: RePEc:eee:reensy:v:212:y:2021:i:c:s0951832021000697
    DOI: 10.1016/j.ress.2021.107507
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    References listed on IDEAS

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    1. Kucherenko, S. & Rodriguez-Fernandez, M. & Pantelides, C. & Shah, N., 2009. "Monte Carlo evaluation of derivative-based global sensitivity measures," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1135-1148.
    2. Sobol’, I.M. & Tarantola, S. & Gatelli, D. & Kucherenko, S.S. & Mauntz, W., 2007. "Estimating the approximation error when fixing unessential factors in global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 92(7), pages 957-960.
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    Cited by:

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    5. Zha, Congyi & Pan, Chenrong & Sun, Zhili & Liu, Qin, 2024. "A single-loop reliability sensitivity analysis strategy for time-dependent rare events with both random variables and stochastic processes," Reliability Engineering and System Safety, Elsevier, vol. 251(C).

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