Transformer networks sample up to 180x180 2D Ising systems and 64x64 Edwards-Anderson systems by generating spin groups with probability approximations, yielding ~20x higher effective sample size than prior neural samplers at criticality.
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Diffusion models suffer critical slowing down when sampling near criticality in the O(n) model but deeper local architectures reduce training-time scaling from quadratic to logarithmic in system size.
Incorporating probability priors into variational autoregressive networks reduces training burden and enables larger system sizes for sampling in the Ising and Edwards-Anderson models.
citing papers explorer
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Sampling two-dimensional spin systems with transformers
Transformer networks sample up to 180x180 2D Ising systems and 64x64 Edwards-Anderson systems by generating spin groups with probability approximations, yielding ~20x higher effective sample size than prior neural samplers at criticality.
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The critical slowing down in diffusion models
Diffusion models suffer critical slowing down when sampling near criticality in the O(n) model but deeper local architectures reduce training-time scaling from quadratic to logarithmic in system size.
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Variational Autoregressive Networks with probability priors
Incorporating probability priors into variational autoregressive networks reduces training burden and enables larger system sizes for sampling in the Ising and Edwards-Anderson models.