MAGPI: Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data

MAGPI: Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data explores Develops a novel multifidelity approach for Gaussian Process Regression to create more accurate and cost-effective surrogate models from scarce high-fidelity data, augmented by cheaper low-fidelity data.. Commercial viability score: 7/10 in Surrogate Modeling.