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.
Deep Gaussian process models for integrating mul- tifidelity experiments with nonstationary relationships.IISE Transactions, 54(7):686– 698, 2022
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ME 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.