An efficient method for moment-independent global sensitivity analysis by dimensional reduction technique and principle of maximum entropy

摘要

Probability density function (PDF)-based and failure probability (FP)-based moment-independent global sensitivity indices can commendably reflect the influence of model input on the whole distribution and partial distribution (or called FP) of model output respectively, yet how to efficiently and accurately estimate these two indices for guiding the engineering practice still remains an essential and challenging problem. In this paper, a novel PDF estimation based method is proposed, which equivalently transforms the computation of these two indices into that of the unconditional and conditional fractional moments of model output. To estimate them, an efficient and simple way is introduced based on a multiplicative version of the dimensional reduction method. The proposed method remarkably reduces the computational cost and can obtain these two indices simultaneously by reusing the information in the integration grid. Results of three case studies demonstrate the effectiveness of the proposed method and its good engineering application.