Multivariate global sensitivity analysis for dynamic models based on energy distance

Abstract

In this paper, a new kind of multivariate global sensitivity index based on energy distance is proposed. The covariance decomposition based index has been widely used for multivariate global sensitivity analysis. However, it just considers the variance of multivariate model output and ignores the correlation between different outputs. The proposed index considers the whole probability distribution of dynamic output based on characteristic function and contains more information of uncertainty than the covariance decomposition based index. The multivariate probability integral transformation based index is an extension of the popularly used moment-independent sensitivity analysis index. Although it considers the whole probability distribution of dynamic output, it is difficult to estimate the joint cumulative distribution function of dynamic output. The proposed sensitivity index can be easily estimated, especially for models with high dimensional outputs. Compared to the classic sensitivity indices, the proposed sensitivity index can be easily used for dynamic systems and obtain reasonable results. An efficient method based on the idea of the given-data method is used to estimate the proposed sensitivity index with only one set of input-output samples. The numerical and engineering examples are employed to compare the proposed index and the covariance decomposition based index. The results show that the input variables may have different effect on the whole probability distribution and variance of dynamic model output since the proposed index and the covariance decomposition based index measure the effects of input variables on the whole distribution and variance of model output separately.