Algorithms based on normalizing flows are emerging as promising machine learning approaches to sampling complicated probability distributions in a way that can be made asymptotically exact. In the context of lattice field theory, proof-of-principle studies have demonstrated the effectiveness of this approach for scalar theories, gauge theories, and statistical systems. This work develops approaches that enable flow-based sampling of theories with dynamical fermions, which is necessary for the technique to be applied to lattice field theory studies of the Standard Model of particle physics and many condensed matter systems. As a practical demonstration, these methods are applied to the sampling of field configurations for a two-dimensional theory of massless staggered fermions coupled to a scalar field via a Yukawa interaction.
翻译:基于正常流流的测算法正在出现,作为一种很有希望的机算学习方法,可以对复杂概率分布进行抽样抽样,这种方式可以不那么精确地加以精确地确定。在拉蒂斯实地理论方面,原则证明研究证明了这一方法对标量理论、测量理论和统计系统的有效性。这项工作开发了一些方法,以便能够对带有动态发酵的理论进行以流动为基础的抽样,这对于将技术应用于粒子物理和许多浓缩物质系统标准模型的拉蒂斯实地理论研究是必要的。作为一种实际的示范,这些方法被用于对野外配置进行抽样,以便通过Yukawa互动将无质量交错的发酵理论与一个标度场结合起来。