Numerical assessment of metamodelling strategies in computationally intensive optimization

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An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, background): “…families of surrogates: (1) response surface surrogates, which are statistical or empirical data-driven models emulating the responses of a high-fidelity simulation model; and (2) lower-fidelity physical based surrogates, which are simplified models of the original system (Razavi et al., 2012b).”
Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling (2014, methodology): “As with any other data-driven model, cross-validation or regularization can be used in the development of ANN models to ensure their generalization ability (Razavi et al., 2012).”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, background): “(Wang and Shan 2007; Kleijnen 2009; Razavi et al. 2012b; Viana et al. 2014).”
Review of surrogate modeling in water resources (2012, background): “The optimal number of hidden neurons, m, is a function of shape and complexity of the underlying function [Xiang et al., 2005] as well as the training data availability [Razavi et al., 2012].”