BWhat changed
Packages neural-operator architectures, spectral/integral layers, data processing, training, losses and memory-efficient tensorized/distributed workflows in a PyTorch-compatible library.
Scientific scope
Packages neural-operator architectures, spectral/integral layers, data processing, training, losses and memory-efficient tensorized/distributed workflows in a PyTorch-compatible library. Software contribution is distinct from implemented GKN/FNO/SFNO/TFNO algorithms and receives no invention credit for them. Discretization behavior remains architecture-, grid- and implementation-dependent; CI claims not independently tested.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Software contribution is distinct from implemented GKN/FNO/SFNO/TFNO algorithms and receives no invention credit for them. Discretization behavior remains architecture-, grid- and implementation-dependent; CI claims not independently tested.
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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