Learning without relearning physical symmetry: an SO(3)-equivariant convolutional network (CNN) on the sphere

Many of the most important datasets in the physical sciences live natively on the sphere, and these data often carry no preferred position or orientation. Using methods that do not reflect this symmetry leads to far higher simulation requirements, which can be catastrophic for scientific analyses (f