Gaussian process regression reconstructs pressure from error-embedded gradients by treating the field as a random process with a fitted correlation kernel, generalizing Green's function integration as its zero-noise limit and outperforming it under noise with calibrated uncertainty.
arXiv:2212.12474 , year=
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Pressure reconstruction from error-embedded gradient measurements: a Gaussian-process generalization of Green's function integration
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