Hierarchical adaptive experimental design for Gaussian process emulators

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Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities (2020, methodology): “With a proper selection of Gaussian process kernel and corresponding hyperparameters, GPR and its variants such as active learning Kriging (ALK), can provide accurate response function estimation and uncertainty quantification [86], and they are increasingly used in engineering surrogate modeling an”
Variance-based sensitivity analysis of model outputs using surrogate models (2011, methodology): “Busby (2009) developed a sequential adaptive design of experiment to obtain a Gaussian emulator using the least possible number of function evaluation.”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, methodology): “Moreover, Busby (2009) modified this approach by replacing the maximum entropy criterion with the maximin distance criterion (Johnson et al. 1990).”
LIF: A new Kriging based learning function and its application to structural reliability analysis (2017, methodology): “Besides meta-model formulation, the design of experiments (DoE) also has huge influence on the convergence rate and the accuracy of reliability analysis [12,15,24,29,37,38].”