Statistical tools for simulation practitioners
Statistical tools for simulation practitioners
Bayesian methods for adaptive models
Bayesian methods for adaptive models
Smoothing noisy data by kriging with nugget effects
An international conference on curves and surfaces on Wavelets, images, and surface fitting
A generalized discrepancy and quadrature error bound
Mathematics of Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Test Examples for Nonlinear Programming Codes
Test Examples for Nonlinear Programming Codes
Metamodeling using extended radial basis functions: a comparative approach
Engineering with Computers
Design and Modeling for Computer Experiments (Computer Science & Data Analysis)
Design and Modeling for Computer Experiments (Computer Science & Data Analysis)
A comparative study of metamodeling methods for multiobjective crashworthiness optimization
Computers and Structures
A stability investigation of a simulation- and reliability-based optimization
Structural and Multidisciplinary Optimization
Structural and Multidisciplinary Optimization
Expert Systems with Applications: An International Journal
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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.