Continuous-Mixture Autoregressive Networks Learning the Kosterlitz-Thouless Transition

نویسندگان

چکیده

We develop deep autoregressive networks with multi channels to compute many-body systems continuous spin degrees of freedom directly. As a concrete example, we demonstrate the two-dimensional XY model continuous-mixture and rediscover Kosterlitz–Thouless (KT) phase transition on periodic square lattice. Vortices characterizing quasi-long range order are accurately detected by generative model. By learning microscopic probability distributions from macroscopic thermal distribution, trained as an efficient physical sampler which can approximate free energy estimate thermodynamic observables unbiasedly importance sampling. more precise evaluation, helicity modulus determine KT temperature. Although training process becomes time-consuming larger lattice sizes, time remains unchanged around The developed thus be potentially used study other freedom.

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ژورنال

عنوان ژورنال: Chinese Physics Letters

سال: 2022

ISSN: ['0256-307X', '1741-3540']

DOI: https://doi.org/10.1088/0256-307x/39/12/120502