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Extension of the RBD-FAST method to the computation of global sensitivity indices

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  • Mara, Thierry Alex

Abstract

This paper deals with the sensitivity analysis method named Fourier amplitude sensitivity test (FAST). This method is known to be very robust for the computation of global sensitivity indices but their computational cost remains prohibitive for complex and large dimensional models. Recent developments in the implementation of FAST by use of the random balance designs (RBD) technique have allowed significant reduction of the computational cost. The method is now called RBD-FAST. The drawback of this improvement is that only individual first-order sensitivity indices can be computed. In this article, an extension of RBD is derived for the estimation of any global sensitivity indices of individual factor or group of factors. Several tests are proposed to compare the performances of classical FAST and RBD-FAST.

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  • Mara, Thierry Alex, 2009. "Extension of the RBD-FAST method to the computation of global sensitivity indices," Reliability Engineering and System Safety, Elsevier, vol. 94(8), pages 1274-1281.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:8:p:1274-1281
    DOI: 10.1016/j.ress.2009.01.012
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    1. Xu, Chonggang & Gertner, George Zdzislaw, 2008. "A general first-order global sensitivity analysis method," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 1060-1071.
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    Cited by:

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    2. Tissot, Jean-Yves & Prieur, Clémentine, 2012. "Bias correction for the estimation of sensitivity indices based on random balance designs," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 205-213.
    3. 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.
    4. Wei, Pengfei & Lu, Zhenzhou & Song, Jingwen, 2015. "Variable importance analysis: A comprehensive review," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 399-432.
    5. Mara, Thierry A. & Tarantola, Stefano, 2012. "Variance-based sensitivity indices for models with dependent inputs," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 115-121.
    6. Mirko Ginocchi & Ferdinanda Ponci & Antonello Monti, 2021. "Sensitivity Analysis and Power Systems: Can We Bridge the Gap? A Review and a Guide to Getting Started," Energies, MDPI, vol. 14(24), pages 1-59, December.

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