Journal
STRUCTURAL SAFETY
Volume 84, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.strusafe.2020.101937
Keywords
Active learning; Distribution function; Gaussian process model; Rare event simulation; Uncertainty quantification
Categories
Funding
- National Science and Technology Major Project of the Ministry of Science and Technology of China [2016YFB0200605]
- National Natural Science Foundation of China [1808149]
- Natural Science Foundation of Guangdong Province [2018A030310067]
- European Union [691728]
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This paper proposes an active learning-based Gaussian process (AL-GP) metamodelling method to estimate the cumulative as well as complementary cumulative distribution function (CDF/CCDF) for forward uncertainty quantification (UQ) problems. Within the field of UQ, previous studies focused on developing AL-GP approaches for reliability (rare event probability) analysis of expensive black-box solvers. A naive iteration of these algorithms with respect to different CDF/CCDF threshold values would yield a discretized CDF/CCDF. However, this approach inevitably leads to a trade off between accuracy and computational efficiency since both depend (in opposite way) on the selected discretization. In this study, a specialized error measure and a learning function are developed such that the resulting AL-GP method is able to efficiently estimate the CDF/CCDF for a specified range of interest without an explicit dependency on discretization. Particularly, the proposed AL-GP method is able to simultaneously provide accurate CDF and CCDF estimation in their median-low probability regions. Three numerical examples are introduced to test and verify the proposed method.
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