Representing and Learning Functions Invariant Under Crystallographic Groups

• URL: http://arxiv.org/abs/2306.05261v1
• Date: Thu, 8 Jun 2023 15:02:04 GMT
• Title: Representing and Learning Functions Invariant Under Crystallographic Groups
• Authors: Ryan P. Adams and Peter Orbanz
• Abstract summary: Crystallographic groups describe the symmetries of crystals and other repetitive structures encountered in nature and the sciences. We derive linear and nonlinear representations of functions that are (1) smooth and (2) invariant under such a group. We show that such a basis exists for each crystallographic group, that it is orthonormal in the relevant $L$ space, and recover the standard Fourier basis as a special case for pure shift groups.
• Score: 18.6870237776672
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Crystallographic groups describe the symmetries of crystals and other repetitive structures encountered in nature and the sciences. These groups include the wallpaper and space groups. We derive linear and nonlinear representations of functions that are (1) smooth and (2) invariant under such a group. The linear representation generalizes the Fourier basis to crystallographically invariant basis functions. We show that such a basis exists for each crystallographic group, that it is orthonormal in the relevant $L_2$ space, and recover the standard Fourier basis as a special case for pure shift groups. The nonlinear representation embeds the orbit space of the group into a finite-dimensional Euclidean space. We show that such an embedding exists for every crystallographic group, and that it factors functions through a generalization of a manifold called an orbifold. We describe algorithms that, given a standardized description of the group, compute the Fourier basis and an embedding map. As examples, we construct crystallographically invariant neural networks, kernel machines, and Gaussian processes.

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