SO(3)-Equivariant Neural Networks for Learning from Scalar and Vector Fields on Spheres

ArXi:2503.09456v2 Announce Type: replace Analyzing scalar and vector fields on the sphere, such as temperature or wind speed and direction on Earth, is a difficult task. Models should respect both the rotational symmetries of the sphere and the inherent symmetries of the vector fields. A class of equivariant models has emerged, which process these spherical signals by applying group convolutions in Fourier space with respect to the three-dimensional rotation group.