cuequivariance

To build equivariant neural network layers, use this framework to manage irreducible representations and segmented tensor products. It constructs operations respecting SO3, O3, and SU2 symmetry constraints for geometric deep learning in molecular and physical modeling.

Irreducible representations are mathematical abstractions used to define custom Lie groups like SO3 and SU2. They manage symmetry constraints with multiplicities to construct complex equivariant operations using Clebsch-Gordan coefficients and segmented tensor products.

Implement Clebsch-Gordan coefficients for tensor products by utilizing the segmented polynomial generation features. This framework computes high-performance tensor contractions and descriptor-based equivariant polynomials for custom geometric neural network layers.

Yes, you can define custom SO3 representations using numpy, which is the required dependency. This allows you to build fully connected tensor products and manage group theory abstractions for scientific machine learning tasks.

You need group-equivariant neural networks when predicting molecular structures requires respecting physical symmetries. This framework provides the necessary group theory abstractions and tensor contractions to enforce these constraints during model training.