DIF-FNO: Diffeomorphic Fourier Neural Operator with D'Agnese Barrier Loss
Official v1.0.0-sota-proof release for DIF-FNO (Diffeomorphic Fourier Neural Operator) powered by the D'Agnese Topological Barrier Loss. This architecture strictly resolves grid-folding anomalies (det(J_phi) > 0) across non-convex computational domains (NACA 0012, Star, L-Shape, Annulus) and high-shear physics boundary layers. Key Benchmark Results:- Absolute Topological Integrity: 0.00% grid folding guaranteed across all resolutions.- Zero-Shot Super-Resolution: Seamless extrapolation from 128x128 to 1024x1024 resolution.- Constant Memory Footprint: Fixed 4.53 MB model memory.- 1-Click Verification Suite: Includes run_all_proofs.py for deterministic reproduction. GitHub Repository: https://github.com/GiovanniDAgnese-paper/DIF-FNO
Authors
- GIOVANNI D'AGNESE
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-08-27
- DOI
- https://doi.org/10.5281/zenodo.22129812
- Primary Topic
- Quantum many-body systems
- Type
- preprint