Update strategies for kriging models used in variable fidelity optimization

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Quantile-Based Optimization of Noisy Computer Experiments With Tunable Precision (2013, methodology): “Most approaches combining optimization and variable precision are found in the multifidelity framework (Gano et al. 2006; Forrester, Sóbester, and Keane 2007) but consider only two fidelity levels, the lowfidelity model being used as a helping tool to choose the highfidelity evaluations.”
Review of surrogate modeling in water resources (2012, methodology): “…utilizing lower-fidelity models are concerned with developing optimization strategies where low- and high-fidelity models are adaptively chosen to be evaluated in the course of optimization [Forrester et al., 2007; Gano et al., 2006; Huang et al., 2006; Leary et al., 2003; Viana et al., 2009].”
Improving variable-fidelity modelling by exploring global design space and radial basis function networks for aerofoil design (2015, methodology): “Hybrid scaling Gano, Renaud, and Sanders (2005) and Gano et al. (2006) proposed a hybrid approach for scaling management that uses the advantages of both of the scaling models: fhigh(x) = ω(β(x)flow(x))+ (1 − ω)(flow(x)+ γ (x)).”
A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations (2013, methodology): “For instance, Alexandrov et al. (2000) and Gano et al. (2006) used polynomial response surfaces and kriging, respectively.”