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It computes\nvariational free energy, estimates physical quantities such as entropy,\nmagnetizations and correlations, and generates uncorrelated samples all at\nonce. Training of the network employs the policy gradient approach in\nreinforcement learning, which unbiasedly estimates the gradient of variational\nparameters. We apply our approach to several classic systems, including 2D\nIsing models, the Hopfield model, the Sherrington-Kirkpatrick model, and the\ninverse Ising model, for demonstrating its advantages over existing variational\nmean-field methods. Our approach sheds light on solving statistical physics\nproblems using modern deep generative neural networks.","url_abs":"http://arxiv.org/abs/1809.10606v2","url_pdf":"http://arxiv.org/pdf/1809.10606v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"solving-statistical-mechanics-using","repo_url":"https://github.com/wangleiphy/VAN.jl","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"solving-statistical-mechanics-using","repo_url":"https://github.com/wdphy16/stat-mech-van","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"variational-monte-carlo","task_name":"Variational Monte Carlo"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.10606","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.10606"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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