Spectral-Domain Deep Learning of Intrinsic Scattering Operators for Arbitrarily Shaped Compact 3D Particles

Abstract

Rapid prediction of optical scattering from arbitrarily shaped three-dimensional particles is important for particle optics and photonic characterization, but remains challenging because of the large variability of complex morphologies and the strong angular dependence of their scattering responses. To address both issues, a dual spectral-domain neural scattering model is introduced in which morphology and scattering are represented in physically ordered bases: particle geometry is compressed into only 256 spherical-harmonic coefficients, and the optical response is encoded by the complex T-matrix in a spherical-vector-wave basis. The morphology spectrum replaces high-dimensional Euclidean geometry representations, such as voxel grids, point clouds, or meshes, with a compact ordered descriptor, while the T-matrix represents a geometry-determined scattering operator that can be queried for different incidence directions, polarizations, and observation angles. A spectral-token Transformer trained on 50{,}000 irregular particles at 1064~nm maps the morphology spectrum directly to the T-matrix. The predicted operators recover modal structure and reproduce full-angle differential scattering maps and incidence-angle scans. Generalization to out-of-distribution synthetic shapes and natural sand-particle morphologies shows that the dual spectral architecture learns an intrinsic relation from the geometry spectrum to multipolar scattering. This establishes spectral-domain operator learning as a compact route for reusable, angle- and polarization-resolved optical scattering prediction of complex 3D particles.