论文标题
通过解释暹罗神经网络来发现对称不变的数量和保守数量
Discovering Symmetry Invariants and Conserved Quantities by Interpreting Siamese Neural Networks
论文作者
论文摘要
在本文中，我们将可解释的暹罗神经网络（SNN）引入了理论物理领域，以进行相似性检测。更确切地说，我们将SNN应用于特殊相对论的事件，电磁场的转化以及颗粒在中心电势中的运动。在这些示例中，SNN学会识别属于相同事件，现场配置或运动轨迹的数据点。事实证明，在学习哪个数据点属于同一事件或字段配置的过程中，这些SNN还学习相关的对称不变性和保守数量。这些SNN是高度可解释的，这使我们能够在没有先验知识的情况下揭示对称性不变性和保守数量。
In this paper, we introduce interpretable Siamese Neural Networks (SNN) for similarity detection to the field of theoretical physics. More precisely, we apply SNNs to events in special relativity, the transformation of electromagnetic fields, and the motion of particles in a central potential. In these examples, the SNNs learn to identify datapoints belonging to the same events, field configurations, or trajectory of motion. It turns out that in the process of learning which datapoints belong to the same event or field configuration, these SNNs also learn the relevant symmetry invariants and conserved quantities. These SNNs are highly interpretable, which enables us to reveal the symmetry invariants and conserved quantities without prior knowledge.
