Data-Driven Prediction of OAM Mode-Purity Spectra for Vortex-Wave Scattering from Metallic Targets

Electromagnetic vortex waves carrying orbital angular momentum (OAM) provide an additional modal dimension for electromagnetic scattering analysis, but the resulting OAM mode-purity spectra are highly nonlinear and expensive to characterize through repeated full-parameter simulations. To address this issue, this work proposes a dual-path data-driven surrogate framework for the simulation-level prediction of OAM mode-purity spectra in metallic-target vortex-wave scattering. High-frequency datasets were generated within a prescribed workflow that combined an angular-spectrum formulation of Bessel vortex beams with a facet-based physical-optics method. Five representative metallic targets were considered, namely, Plate, Spiral, Spite, Missile, and Dihedral. In the first surrogate path, a numerical-parameter-based regression model was developed to predict the mode-purity spectrum from physical scattering variables for canonical targets. In the second surrogate path, a phase-map-based regression model was introduced to predict the spectrum directly from scattered-field phase maps without explicit geometric parameterization. The results show that the parameter-based surrogate achieves low prediction errors for canonical targets, while the proposed ConvNeXt + GAM model provides strong regression performance across multiple target categories in the phase-map-based setting. Overall, the proposed framework offers an efficient surrogate approximation of the nonlinear mapping between the scattering conditions and OAM mode-purity spectra under simulated conditions. This study is positioned as a simulation-level surrogate modeling investigation, and extension to experimental measurements or real-scene applications remains as future work.