An ensemble of hierarchical kriging emulators aggregated by Bayesian model averaging yields accurate multi-fidelity predictions with uncertainty-driven adaptive sampling that outperforms single models on benchmarks.
Dicedesign and diceeval: Two R packages for design and analysis of computer experiments.Journal of Statistical Software, 65(11):1–38, 2015
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An ensemble-based approach for multi-fidelity emulation and adaptive sampling
An ensemble of hierarchical kriging emulators aggregated by Bayesian model averaging yields accurate multi-fidelity predictions with uncertainty-driven adaptive sampling that outperforms single models on benchmarks.