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• W4385989185 startingPage "109545" @default.
• W4385989185 abstract "To alleviate the computational overhead of high-dimensional reliability analysis, a cost-effective surrogate model called the PPCK is proposed by combining partial least squares (PLS) and polynomial chaos Kriging (PCK) in a non-intrusive way. Three major contributions are made in the PPCK. First, when calibrating a PCK in the PLS-based reduced space, the multivariate polynomial basis orthonormal with respect to those reduced variables is built by a data-driven approach. Second, to identify the optimal number of reduced variables, rational identification criteria are defined for three different performance metrics. Third, a parallel procedure coupled with adaptive adjustment is devised to replace the traditional progressive addition of reduced variables, so as to accelerate the workflow of PPCK. The performances of both the proposed PPCK and the associated reliability algorithm are illustrated on one benchmark analytical function and two practical engineering problems. The results show that, different from the PCK whose training time rises dramatically during adaptive enrichment process, the PPCK provides sufficient predictive accuracy, but maintains relatively low training time consistently. Then, the PPCK-based reliability algorithm achieves favorable savings in terms of both the number of computational model evaluation and total computational time." @default.
• W4385989185 created "2023-08-19" @default.
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• W4385989185 date "2023-12-01" @default.
• W4385989185 modified "2023-10-14" @default.
• W4385989185 title "Partial least squares-based polynomial chaos Kriging for high-dimensional reliability analysis" @default.
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• W4385989185 doi "https://doi.org/10.1016/j.ress.2023.109545" @default.
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