Abstract: In this talk, we discuss Siamese Neural Networks (SNN) for similarity detection and apply them to examples in physics. These examples include special relativity, electromagnetism, and motion in a gravitational potential. The SNNs learn to identify data points belonging to the same events, field configurations, or trajectory of motion. In the process of learning which data points belong to the same event or field configuration, these SNNs also learn the relevant symmetry invariants and conserved quantities, which can be revealed by interpreting the latent space of the SNNs.