A comparative study of metamodeling methods considering sample quality merits

This research focuses on the study of the relationships between sample data characteristics and metamodel performance considering different types of metamodeling methods. In this work, four types of metamodeling methods, including multivariate polynomial method, radial basis function method, kriging method and Bayesian neural network method, three sample quality merits, including sample size, uniformity and noise, and four performance evaluation measures considering accuracy, confidence, robustness and efficiency, are considered. Different from other comparative studies, quantitative measures, instead of qualitative ones, are used in this research to evaluate the characteristics of the sample data. In addition, the Bayesian neural network method, which is rarely used in metamodeling and has never been considered in comparative studies, is selected in this research as a metamodeling method and compared with other metamodeling methods. A simple guideline is also developed for selecting candidate metamodeling methods based on sample quality merits and performance requirements.