Global response approximation with radial basis functions

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Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions (2010, background): “…and Shan (2007) listed popular models, such as kriging models (Joseph et al. 2006; Martin and Simpson 2005), radial basis functions (RBF) models (Fang and Horstemeyer 2006; Regis and Shoemaker 2007a, b), response surface models (Hill and Hunter 1966; Kaufman et al. 1996), support vector machine…”
Global optimization of expensive black box functions using potential Lipschitz constants and response surfaces (2015, background): “Since the recently developed response surfaces (also known as metamodels) such as the Kriging model [31] and the RBF model [32] are able to approximate the real function f in a smooth, compact way, we state that the metamodeling techniques can help to estimate more compact lower bounds.”
Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment (2017, background): “It should be noted that an RBF passes through all the sampling points exactly.”
Review of Metamodeling Techniques in Support of Engineering Design Optimization (2007, methodology): “Other types of models include Radial Basis Functions (RBF) [51, 52], Multivariate Adaptive Regression Splines (MARS) [53], Least Interpolating”