Gauge-equivariant graph neural networks embed non-Abelian local symmetries directly into message passing for lattice gauge theories, enabling learning of nonlocal observables from local operations.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
mHIP-NN extends hierarchical message-passing networks to model electron-mediated spin dynamics in disordered itinerant magnets while preserving rotational symmetry.
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Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories
Gauge-equivariant graph neural networks embed non-Abelian local symmetries directly into message passing for lattice gauge theories, enabling learning of nonlocal observables from local operations.
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Magnetic HIP-NN for spin dynamics in disordered itinerant magnets
mHIP-NN extends hierarchical message-passing networks to model electron-mediated spin dynamics in disordered itinerant magnets while preserving rotational symmetry.