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

MAST is a new multi-fidelity surrogate model that improves upon existing methods by effectively combining low- and high-fidelity data through spatial trust-weighting, enhancing accuracy while managing computational costs efficiently. This advancement is crucial for content creators in engineering and scientific computing as it allows for more reliable predictions under tight budget constraints without sacrificing performance.

Read the full article at arXiv cs.LG (ML)

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