Accelerated Monte Carlo simulations with restricted Boltzmann machines
Accelerated Monte Carlo simulations with restricted Boltzmann machines
复制标题
使用受限玻尔兹曼机加速蒙特卡罗模拟
DOI：
10.1103/physrevb.95.035105
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发表时间：
2017-01-04
影响因子：
3.7
通讯作者：
Wang, Lei
中科院分区：
文献类型：
--
作者：
Huang, Li;Wang, Lei
尽管蒙特卡罗方法具有特殊的灵活性和流行性，但在解决具有挑战性的统计物理问题时，它往往会遇到混合时间慢的问题。我们提出了一个通用策略，通过采用机器学习社区的思想和技术来克服这一困难。我们将物理模型的非归一化概率拟合到前馈神经网络中，并将其重新解释为受限玻尔兹曼机。然后，利用其特征检测能力，我们利用限制玻尔兹曼机进行有效的蒙特卡罗更新，并加快模拟原始的物理系统。我们实现了Falicov-Kimball模型的这些想法，并证明了相变点附近的接受率和自相关时间的改善。
Despite their exceptional flexibility and popularity, the Monte Carlo methods often suffer from slow mixing times for challenging statistical physics problems. We present a general strategy to overcome this difficulty by adopting ideas and techniques from the machine learning community. We fit the unnormalized probability of the physical model to a feedforward neural network and reinterpret the architecture as a restricted Boltzmann machine. Then, exploiting its feature detection ability, we utilize the restricted Boltzmann machine for efficient Monte Carlo updates and to speed up the simulation of the original physical system. We implement these ideas for the Falicov-Kimball model and demonstrate improved acceptance ratio and autocorrelation time near the phase transition point.