A Comparison Of Meta -modeling Methods Using Practical Industry Requirements

In spite of the exponential growth of computing power, the enormous computational cost of complex and large -scale engineering design problems make it impractical to rely exclusively on original high fidelity simulation codes. Performing probabilistic and robust design on these long running applications is still a formidable task in any industry. The objective of this paper is to investigate the advantages and disad vantages of the several promising meta -modeling techniques under practical industry requirements. The comparisons are based on a set of performance criteria that are known to be important to industry. The meta -models include Multivariate Adaptive Regressio n Splines (MARS), Radial Basis Functions (RBF), Adaptive Weighted Least Squares (AWLS), Gaussian Process (GP), and quadratic response surface regression (RSM). Twenty benchmark problems representing different levels of dimensionality, non -linearity and noi se level are used for the evaluation.

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