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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A gauge-invariant GNN using Wilson loops as inputs accurately predicts observables and simulates dynamics in Z2 and U(1) lattice gauge models.
Neural networks trained with 10-100x fewer examples than prior work approximate CT-QMC impurity solvers in DMFT, delivering comparable accuracy on interpolation and accelerating simulations up to 5x when used as initial guesses for lower temperatures.
Machine learning on simulation observables produces a phase diagram for the Vicsek model that identifies a narrow coexistence region between ordered and disordered states.
citing papers explorer
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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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Graph Neural Networks in the Wilson Loop Representation of Abelian Lattice Gauge Theories
A gauge-invariant GNN using Wilson loops as inputs accurately predicts observables and simulates dynamics in Z2 and U(1) lattice gauge models.
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Neural networks as low-cost surrogates for impurity solvers in quantum embedding methods
Neural networks trained with 10-100x fewer examples than prior work approximate CT-QMC impurity solvers in DMFT, delivering comparable accuracy on interpolation and accelerating simulations up to 5x when used as initial guesses for lower temperatures.
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Mapping the Phase Diagram of the Vicsek Model with Machine Learning
Machine learning on simulation observables produces a phase diagram for the Vicsek model that identifies a narrow coexistence region between ordered and disordered states.