论文标题
随机归一化流作为非平衡转化
Stochastic normalizing flows as non-equilibrium transformations
论文作者
论文摘要
标准化流是一类深入生成模型，比传统的蒙特卡洛模拟更有效地为样品晶格场理论提供了有希望的途径。在这项工作中，我们表明，随机归一化流的理论框架，其中神经网络层与蒙特卡洛更新结合在一起，与基于jarzynski平等的不平衡外模拟的基础相同，这些模拟最近已部署以计算lattice级别的自由范围差异。我们制定了一种策略，以优化这种扩展类别的生成模型的效率和应用程序的示例。
Normalizing flows are a class of deep generative models that provide a promising route to sample lattice field theories more efficiently than conventional Monte Carlo simulations. In this work we show that the theoretical framework of stochastic normalizing flows, in which neural-network layers are combined with Monte Carlo updates, is the same that underlies out-of-equilibrium simulations based on Jarzynski's equality, which have been recently deployed to compute free-energy differences in lattice gauge theories. We lay out a strategy to optimize the efficiency of this extended class of generative models and present examples of applications.
