MAST: A Multi-fidelity Augmented Surrogate model via Spatial Trust-weighting

ArXi:2602.20974v2 Announce Type: replace In engineering design and scientific computing, computational cost and predictive accuracy are intrinsically coupled. High-fidelity simulations provide accurate predictions but at substantial computational costs, while lower-fidelity approximations offer efficiency at the expense of accuracy. Multi-fidelity surrogate modelling addresses this trade-off by combining abundant low-fidelity data with sparse high-fidelity observations.