Equivariant Neural Networks

Equivariant neural networks are a class of neural network architectures designed using group-theoretic constraints, enabling models to inherently satisfy symmetries such as rotation, translation, and reflection. As a hard-constraint approach, they are widely used in scientific computing applications requiring physical symmetry, including molecular dynamics and physics simulation.