Augmenting a high-fidelity GP's inputs with predictions from all low-fidelity surrogates improves accuracy and cuts cost versus cokriging and autoregressive multifidelity GPs on scarce-data problems.
Optimal Model Management for Multifidelity Monte Carlo Estimation,
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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data
Augmenting a high-fidelity GP's inputs with predictions from all low-fidelity surrogates improves accuracy and cuts cost versus cokriging and autoregressive multifidelity GPs on scarce-data problems.