Cellular Sheaf Neural Operators use cell complexes, learned restriction maps, and structure-aware message passing to create discretization-aware neural surrogates that preserve constraints in multiphysics PDEs such as MHD.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
TopoGeoScore learns a non-negative linear combination of geometric and topological features from source embeddings via self-supervised invariance to select robust checkpoints for OOD scenarios.
citing papers explorer
-
Cellular Sheaf Neural Operators for Structure-Preserving Surrogate Modeling of Constrained PDEs
Cellular Sheaf Neural Operators use cell complexes, learned restriction maps, and structure-aware message passing to create discretization-aware neural surrogates that preserve constraints in multiphysics PDEs such as MHD.
-
TopoGeoScore: A Self-Supervised Source-Only Geometric Framework for OOD Checkpoint Selection
TopoGeoScore learns a non-negative linear combination of geometric and topological features from source embeddings via self-supervised invariance to select robust checkpoints for OOD scenarios.