Metamodeling: Radial basis functions, versus polynomials

Abstract

For many years, metamodels have been used in simulation to provide approximations to the input–output functions provided by a simulation model. In this paper, metamodels based on radial basis functions are applied to approximate test functions known from the literature. These tests were conducted to gain insights into the behavior of these metamodels, their ease of computation and their ability to capture the shape and minima of the test functions. These metamodels are compared against polynomial metamodels by using surface and contour graphs of the error function (difference between metamodel and the given function) and by evaluating the numerical stability of the required computations. Full factorial and Latin hypercube designs were used to fit the metamodels. Graphical and statistical methods were used to analyze the test results. Factorial designs were generally more successful for fitting the test functions as compared to Latin hypercube designs. Radial basis function metamodels using factorial and Latin hypercube designs provided better fit than polynomial metamodels using full factorial designs.