The use of metamodeling techniques for optimization under uncertainty

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Level set based robust shape and topology optimization under random field uncertainties (2010, neutral): “constant c [35-40].”
Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment (2017, methodology): “In case of physical or noisy computer experiments, it is found that Kriging and RBF are more sensitive to numerical noise than polynomial models [103].”
On Sequential Sampling for Global Metamodeling in Engineering Design (2002, background): “Recent studies on using metamodels in design applications include [2, 3, 4, 5, 6], etc.”
Review of Metamodeling Techniques in Support of Engineering Design Optimization (2007, methodology): “In case of physical or noisy computer experiments, it is found that Kriging and RBF are more sensitive to numerical noise than polynomial models [13, 81].”
A survey on approaches for reliability-based optimization (2010, methodology): “The application of meta-modeling techniques has allowed replacing numerically intensive virtual simulation models by inexpensive ones (see, e.g. Hurtado (2004); Jin et al (2003); Papadrakakis and Lagaros (2002); Zhang and Foschi (2004)).”