AWhat changed
Adds resolution-scaled differential stencil layers and compact-support integral kernels using discrete–continuous convolution bases to neural operators, capturing local detail alongside global spectral layers.
Scientific scope
Adds resolution-scaled differential stencil layers and compact-support integral kernels using discrete–continuous convolution bases to neural operators, capturing local detail alongside global spectral layers. First three authors explicitly share contribution. Differential limits require smooth inputs, regular-grid scaling and kernel constraints; integral layers need suitable quadrature and discretization. Inherits stencils, group convolutions and DISCO; benchmark gains are not certified arbitrary-grid/PDE accuracy or exact finite-discretization equivariance.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. First three authors explicitly share contribution. Differential limits require smooth inputs, regular-grid scaling and kernel constraints; integral layers need suitable quadrature and discretization. Inherits stencils, group convolutions and DISCO; benchmark gains are not certified arbitrary-grid/PDE accuracy or exact finite-discretization equivariance.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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