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• W2801700536 abstract "Numerical moment matching (NMM) technique is a point estimation method that holds significant applicability in the era of large-scale data. One can use NMM to create an extremely small set of representative samples of an engineering population or to facilitate a fast and robust uncertainty quantification of a complex structure. However, the previous NMM method based on the multivariate Newton-Raphson (mNR) scheme often suffers from severe numerical divergence and initial-value dependency. This study overcomes the aforementioned limitations by stabilizing NMM with a genetic algorithm (GA), giving rise to a highly stable and fast NMM (denoted as GA-NMM). Inheriting NMM’s strengths, GA-NMM exhibits no restriction to irregular distributions, large sizes, or many variables of engineering data. This paper elaborates the formulations of GA-NMM along with a practical recommendation for setting optimal parameters. Validations encompass theoretical and practical cases. Simulations with the default setting of GA-NMM demonstrate successful performances in data-squashing of an engineering population and a fast, robust uncertainty quantification of a complex structure. All the developed programs are made publicly available for promoting data-driven research paradigm in broader engineering domains." @default.
• W2801700536 created "2018-05-17" @default.
• W2801700536 creator A5015888108 @default.
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• W2801700536 date "2018-07-01" @default.
• W2801700536 modified "2023-10-16" @default.
• W2801700536 title "Numerical moment matching stabilized by a genetic algorithm for engineering data squashing and fast uncertainty quantification" @default.
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• W2801700536 doi "https://doi.org/10.1016/j.compstruc.2018.04.002" @default.
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