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- W2970964183 abstract "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." @default.
- W2970964183 created "2019-09-05" @default.
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- W2970964183 date "2019-08-27" @default.
- W2970964183 modified "2023-09-27" @default.
- W2970964183 title "A novel active learning-based Gaussian process metamodelling strategy for estimating the full probability distribution in forward UQ analysis" @default.
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- W2970964183 doi "https://doi.org/10.48550/arxiv.1908.10341" @default.
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