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• W2893674448 abstract "We propose a general framework for solving statistical mechanics of systems with finite size. The approach extends the celebrated variational mean-field approaches using autoregressive neural networks, which support direct sampling and exact calculation of normalized probability of configurations. It computes variational free energy, estimates physical quantities such as entropy, magnetizations and correlations, and generates uncorrelated samples all at once. Training of the network employs the policy gradient approach in reinforcement learning, which unbiasedly estimates the gradient of variational parameters. We apply our approach to several classic systems, including 2D Ising models, the Hopfield model, the Sherrington-Kirkpatrick model, and the inverse Ising model, for demonstrating its advantages over existing variational mean-field methods. Our approach sheds light on solving statistical physics problems using modern deep generative neural networks.Received 8 November 2018DOI:https://doi.org/10.1103/PhysRevLett.122.080602© 2019 American Physical SocietyPhysics Subject Headings (PhySH)Physical SystemsArtificial neural networksSpin glassesTechniquesIsing modelVariational approachStatistical Physics" @default.
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• W2893674448 date "2019-02-28" @default.
• W2893674448 modified "2023-10-17" @default.
• W2893674448 title "Solving Statistical Mechanics Using Variational Autoregressive Networks" @default.
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• W2893674448 doi "https://doi.org/10.1103/physrevlett.122.080602" @default.
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