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

机译：将RBD-FAST方法扩展到全局灵敏度指数的计算

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摘要

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.
机译：本文讨论了一种名为傅里叶幅度灵敏度测试（FAST）的灵敏度分析方法。众所周知，该方法对于全局灵敏度指标的计算非常健壮，但是对于复杂和大型模型，其计算成本仍然很高。通过使用随机平衡设计（RBD）技术实现FAST的最新进展已大大降低了计算成本。该方法现在称为RBD-FAST。这种改进的缺点是只能计算单个一阶灵敏度指标。在本文中，RBD的扩展是用于估计单个因素或一组因素的整体敏感性指标。提出了几种测试来比较经典FAST和RBD-FAST的性能。

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