Motility in heterogeneous Kuramoto lattices enables a BKT transition that restores quasi-long-range order, while non-reciprocity in O(2) models selects defect shapes and enriches annihilation dynamics.
Uncertainty in ai-driven monte carlo simulations
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Global Annealing Monte Carlo with ML global moves plus local updates outperforms Simulated Annealing and is more robust than Population Annealing on 3D Ising spin glasses without hyperparameter tuning.
citing papers explorer
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Topological defects in out-of-equilibrium systems
Motility in heterogeneous Kuramoto lattices enables a BKT transition that restores quasi-long-range order, while non-reciprocity in O(2) models selects defect shapes and enriches annihilation dynamics.
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Demonstrating Real Advantage of Machine-Learning-Enhanced Monte Carlo for Combinatorial Optimization
Global Annealing Monte Carlo with ML global moves plus local updates outperforms Simulated Annealing and is more robust than Population Annealing on 3D Ising spin glasses without hyperparameter tuning.