用于任意形状紧凑三维粒子的本征散射算子的谱域深度学习
Spectral-Domain Deep Learning of Intrinsic Scattering Operators for Arbitrarily Shaped Compact 3D Particles
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中文总结 AI 辅助
研究任意形状三维粒子光学散射预测难题，提出双谱域神经散射模型，将粒子几何压缩为球谐系数，光学响应编码为T矩阵，经谱令牌变换器训练，能恢复模态结构、再现散射图，确立谱域算子学习为有效预测途径。
中文摘要 AI 辅助
快速预测任意形状三维粒子的光学散射对粒子光学和光子特性表征很重要，但由于复杂形态的巨大变异性及其散射响应的强角度依赖性，这仍然具有挑战性。为解决这两个问题，引入了一种双谱域神经散射模型，其中形态和散射用物理有序基表示：粒子几何形状被压缩为仅256个球谐系数，光学响应由球矢量波基中的复T矩阵编码。形态谱用紧凑有序描述符取代了高维欧几里得几何表示，而T矩阵表示一个几何确定的散射算子，可针对不同的入射方向偏振和观测角度进行查询。在1064nm波长下对50000个不规则粒子训练的谱令牌变换器将形态谱直接映射到T矩阵。预测的算子恢复了模态结构并再现了全角微分散射图和入射角扫描。对分布外合成形状和天然砂粒形态的泛化表明，双谱架构从几何谱到多极散射学习了一种内在关系。这将谱域算子学习确立为一种紧凑的途径，用于对复杂三维粒子进行可重复使用的、角度和偏振分辨的光学散射预测。
英文摘要
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.
