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.
Experimental and Numerical Study of the Laminar Burning Velocity and Pollutant Emissions of the Mixture Gas of Methane and Carbon Dioxide,
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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.