Addressing factors fixing setting from given data: A comparison of different methods. (January 2017) - Mara, Thierry A.; Belfort, Benjamin; Fontaine, Vincent; Younes, Anis
Abstract: This paper deals with global sensitivity analysis of computer model output. Given an independent input sample and associated model output vector with possibly the vector of output derivatives with respect to the input variables, we show that it is possible to evaluate the following global sensitivity measures: (i) the Sobol' indices, (ii) the Borgonovo's density-based sensitivity measure, and (iii) the derivative-based global sensitivity measure of Sobol' and Kucherenko. We compare the efficiency of the different methods to address factors fixing setting, an important issue in global sensitivity analysis. First, global sensitivity analysis of the Ishigami function is performed with the different methods. Then, they are applied to two different responses of a soil drainage model. The results show that the polynomial chaos expansion for estimating Sobol' indices is the most efficient approach. Highlights: We compare several methods for global sensitivity analysis of model output. All the methods only require one single sample with input variables independently distributed. We study their efficiency to address factors fixing setting. The different methods are tested on the Ishigami function and a soil drainage model. Sobol' indices via sparse PCE are found to be the most efficient approach.