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.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Statistical study of QI stellarator designs shows principal-direction rotation rate of the plasma boundary best predicts coil non-planarity, with surface features yielding Random Forest R²=0.882.
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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.
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Exploring the link between coil non-planarity and magnetic surface geometry across a dataset of QI stellarators
Statistical study of QI stellarator designs shows principal-direction rotation rate of the plasma boundary best predicts coil non-planarity, with surface features yielding Random Forest R²=0.882.