Numerical moment matching stabilized by a genetic algorithm for engineering data squashing and fast uncertainty quantification

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.