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The paper discusses how operator‑learning benchmarks are stored as full‑precision arrays that have grown to terabyte scale, and curators must decide how coarsely to compress them. It argues that the usual tolerance on reconstruction error of the stored field is measured in the wrong place, because what matters is the accuracy of a model trained on the compressed copy, not the raw field error. The authors propose a new metric: push a compressed field through a model trained at full precision and compare its output to the output on the clean field, which captures the portion of codec error that actually reaches the model’s output. This metric spans more than two orders of magnitude across six PDE families and correctly orders downstream cost where field error does not, reversing many cross‑dataset comparisons.
arXiv:2606. 17460v1 Announce Type: new Abstract: Neural operators are widely used as surrogate solution maps for partial differential equations (PDEs), but full-size models can be costly to store, deploy, and evaluate in many-query scientific workflows.
arXiv:2609.24021v1 Announce Type: cross Abstract: Neural operators are data-driven models that learn mappings from inputs that parameterize partial differential equations, such as spatially varying c...
arXiv:2610.17221v1 Announce Type: cross Abstract: Neural surrogates for Partial Differential Equations (PDEs) are usually trained on synthetic data from numerical simulators. As models grow and scali...
Neural operators are data-driven models that learn mappings from inputs that parameterize partial differential equations, such as spatially varying coefficients, initial conditions, forcing terms, bou...
arXiv:2606. 27354v1 Announce Type: cross Abstract: Neural surrogate models offer fast approximate mappings from PDE parameters to solutions, but they typically treat solving as a purely statistical task: once trained, they struggle to correct their own constraint violations and extrapolate beyond the training distribution.