AWhat changed
Jointly parameterizes spectral operator weights through low-rank tensor factorizations and predicts local domains with hierarchical coarse-to-fine global context, reducing parameter and memory costs in reported PDE tasks.
Scientific scope
Jointly parameterizes spectral operator weights through low-rank tensor factorizations and predicts local domains with hierarchical coarse-to-fine global context, reducing parameter and memory costs in reported PDE tasks. 2023 preprint source, four authors. Inherits Tucker/CP/TT decompositions and domain decomposition; low-rank restrictions and empirical compression do not preserve all operators at fixed rank or establish global convergence. Multi-grid context is not a classical V-cycle; source's heuristic heat-kernel localization illustration is not imported as a mathematical certificate.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. 2023 preprint source, four authors. Inherits Tucker/CP/TT decompositions and domain decomposition; low-rank restrictions and empirical compression do not preserve all operators at fixed rank or establish global convergence. Multi-grid context is not a classical V-cycle; source's heuristic heat-kernel localization illustration is not imported as a mathematical certificate.
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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