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MAGPI: Multifidelity-Augmented Gaussian Process Inputs For Surrogate Modeling From Scarce Data by Tuong Cao Gia is a document available to read on EtoBox.

What is MAGPI: Multifidelity-Augmented Gaussian Process Inputs For Surrogate Modeling From Scarce Data about?

The document presents MAGPI, a novel multifidelity Gaussian process regression approach for surrogate modeling that integrates both high- and low-fidelity data to improve predictive accuracy while reducing computational costs. It proposes a method that augments the input space of the surrogate model with features derived from low-fidelity data, addressing challenges in traditional cokriging and autoregressive models. Numerical experiments demonstrate that MAGPI outperforms existing methods in terms of accur

Author

Tuong Cao Gia

Language

EN