Hybrid Variable Fidelity Optimization by Using a Kriging-Based Scaling Function

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Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? (2014, methodology): “[89] built kriging-based scaling functions combined with a trust regionmanaged scheme and proved it to converge to the solution of the higher-fidelity model.”
Update strategies for kriging models used in variable fidelity optimization (2006, methodology): “ Gano et al. (2005b) built kriging-based scaling functions that match low-fidelity models to high-fidelity models, such that the low-fidelity model augmented with the scaling function approximates the high-fidelity model.”
A sequential surrogate method for reliability analysis based on radial basis function (2017, methodology): “In general, the response surface approximation model can be replaced by other surrogate model (also known as meto-model) techniques such as radial basis function (RBF), Kriging, support vector regression (SVR), artificial neutral net (ANN), etc. [18, 33-44].”
Design and Analysis of Computer Experiments in Multidisciplinary Design Optimization: A Review of How Far We Have Come - Or Not (2008, unknown): “Gano et al. 113 enhanced this approach by introducing a metamodel update management scheme based on the trust region ratio to reduce the cost of rebuilding kriging models by updating the kriging model parameters only when they produce a poor approximation.”