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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