A novel single-loop simulation method and its combination with adaptive kriging for moment-independent global sensitivity analysis

Abstract

The moment-independent global sensitivity (MIGS) analysis can help decision makers to efficiently reduce the uncertainty of model output. This article proposes a novel single-loop simulation (NSLS) method for MIGS analysis. Through an interval discretization strategy, estimation of probability density functions (PDFs) in existing methods is transformed to estimation of the marginal probability of the input, the marginal probability of the output and the joint probability of the inputs–output in NSLS, which avoids the computational complexity and error resulting from PDF estimation in MIGS analysis. To drastically reduce the computational cost of NSLS to estimate the MIGS index, an NSLS-based adaptive kriging (NSLS-AK) method is subsequently developed. In NSLS-AK, a rough global kriging model is first constructed, then this kriging model is adaptively updated until the convergent condition is satisfied for accurately and efficiently predicting the responses of the samples in estimating the MIGS index.

KEYWORDS:

• Moment-independent global sensitivity analysis
• marginal and joint probabilities
• single-loop simulation
• adaptive kriging

Acknowledgements

This work was supported by the National Natural Science Foundation of China [grant numbers NSFC 52075442, 12002237] and the National Science and Technology Major Project [2017-IV-0009-0046]. This financial support is gratefully acknowledged.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number: NSFC 52075442, 12002237] and the National Science and Technology Major Project [2017-IV-0009-0046].