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.

翻译：标准化流是一种深层次的基因模型,它比传统的蒙特卡洛模拟法更高效地提供了一种极好的途径,可以用来抽样研究拉蒂斯实地理论。 在这项工作中,我们表明,神经网络层与蒙特卡洛最新信息相结合的随机正常流的理论框架与基于贾钦斯基平等原则的平衡模拟的理论框架相同,后者最近被用于计算拉蒂斯测量理论中的自由能源差异。我们提出了一项战略,以优化这一扩大型基因模型和应用实例的效率。