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.
A survey of ensemble learning: Concepts, algorithms, applications, and prospects.IEEE Access, 10:99129–99149
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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.