Global and local optimization using radial basis function response surface models

By D. McDonald, W. Grantham, Wayne L. Tabor, M. Murphy. 2007

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Computational Reduction for Parametrized PDEs: Strategies and Applications (2012, background): “In the case where the output function s(·) has several local maxima and minima, and the problem involves high dimensions and/or scattered data in the parameter space, radial basis functions (RBF) have been found very accurate to generate response surface models [33].”
A comparative study of metamodeling methods considering sample quality merits (2010, methodology): “Other basis functions, including the multiquadratic model and the thin-plate model ( McDonald et al. 2007 ), were also tested in this work and found less effective in the selected cases.”
Sequential Approximate Optimization using Radial Basis Function network for engineering optimization (2011, methodology): “It is possible to use the Kriging (Donald et al. 1998; Simpson et al. 2001a; Martin and Simpson 2005) and the RBF Network (Muller and Messac 2005; Hussain et al. 2002; Jin et al. 2001; Nakayama et al. 2002; Fang and Horstemeyer 2006; McDonald et al. 2007 ) to approximate the original function local”