Lossy Compression of PDE Training Inputs: Field Reconstruction Error Does Not Order the Cost to a Trained Operator

Abstract:Operator-learning benchmarks are stored at full precision and have grown to terabyte scale. Rate-distortion theory says how many bits the stored field needs, while a practitioner needs to know how accurate an operator trained on the compressed data will be. We show that the first does not determine the second, and measure why, compressing the input fields while targets and test inputs stay at full precision. A solution operator attenuates a perturbation of its input. Pushing a compressed field through a surrogate already trained at full precision measures how much of the perturbation that surrogate transmits. The fraction is consistent with the smoothing behaviour of the underlying equation, and it spans more than two orders of magnitude across PDE families. Field reconstruction error is computed before the attenuation and cannot see it. For operators trained with mean squared error it inverts 36 of 104 cost comparisons across datasets, where a probe built from the same forward passes inverts 12. Two families that PDEBench stores with identical initial conditions differ threefold downstream at identical field error. Under the relative-L2 objective of the reference recipe the separation narrows, while the ordering of the family-level median transmission factors is unchanged. After one full-precision training run, the probe evaluates an entire rate curve by forward passes alone. It ranks datasets and rates consistently across the codecs and architectures we test, while its magnitude does not transfer between them.

Machine Learning (cs.LG)

arXiv:2610.06095 [cs.LG]

(or arXiv:2610.06095v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.06095 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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