论文标题
学习晶格量子场理论，具有等效性连续流
Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
论文作者
论文摘要
我们提出了一种新型的机器学习方法，用于从晶格场理论的高维概率分布中进行取样，该方法基于单个神经ode层，并结合了问题的完整对称性。我们在$ ϕ^4 $理论上测试了我们的模型，这表明它系统地超过了先前提出的基于流动效率的基于流的方法，并且对于较大的晶格而言，改进尤其明显。此外，我们证明我们的模型可以一次学习一个连续的理论家族，并且可以将学习的结果转移到更大的晶格中。这种概括进一步加剧了机器学习方法的优势。
We propose a novel machine learning method for sampling from the high-dimensional probability distributions of Lattice Field Theories, which is based on a single neural ODE layer and incorporates the full symmetries of the problem. We test our model on the $ϕ^4$ theory, showing that it systematically outperforms previously proposed flow-based methods in sampling efficiency, and the improvement is especially pronounced for larger lattices. Furthermore, we demonstrate that our model can learn a continuous family of theories at once, and the results of learning can be transferred to larger lattices. Such generalizations further accentuate the advantages of machine learning methods.
