论文标题
晶格量规的应用神经网络的应用
Applications of Lattice Gauge Equivariant Neural Networks
论文作者
论文摘要
将相关的物理信息引入神经网络体系结构已成为一种广泛使用且成功的策略，以提高其性能。在晶格量规理论中，可以用量规对称性识别此类信息，这些信息被纳入我们最近提出的晶格量规卷积神经网络（L-CNN）的网络层中。 L-CNN可以比传统的神经网络更好地概括到不同大小的晶格，并且是通过在晶格仪转换下进行的施工型。在这些程序中，我们在L-CNN在Wilson流量或连续归一化流程中的可能应用上提出了进度。我们的方法基于神经常见的微分方程，使我们能够以量规模棱两可的方式修改链接配置。为简单起见，我们专注于简单的玩具模型，以在实践中测试这些想法。
The introduction of relevant physical information into neural network architectures has become a widely used and successful strategy for improving their performance. In lattice gauge theories, such information can be identified with gauge symmetries, which are incorporated into the network layers of our recently proposed Lattice Gauge Equivariant Convolutional Neural Networks (L-CNNs). L-CNNs can generalize better to differently sized lattices than traditional neural networks and are by construction equivariant under lattice gauge transformations. In these proceedings, we present our progress on possible applications of L-CNNs to Wilson flow or continuous normalizing flow. Our methods are based on neural ordinary differential equations which allow us to modify link configurations in a gauge equivariant manner. For simplicity, we focus on simple toy models to test these ideas in practice.
