U-PINet: Physics-Informed Hierarchical Learning for Radar Cross Section Prediction via 3D Electromagnetic Scattering Reconstruction

Conventional computational electromagnetics (CEM) solvers can deliver high fidelity radar cross section (RCS) signatures by first solving the induced surface currents on 3-dimensional (3D) targets and then evaluating the scattered fields via radiation integrals. However, their computational cost becomes prohibitive for repeated queries and large-scale 3D scenarios. Recent purely data-driven networks improve efficiency, yet they often bypass this scattering mechanism, which may compromise physical consistency and generalization. To bridge this gap, in this paper, we propose U-PINet, a fully end-to-end, physics-informed hierarchical network for efficient RCS prediction via 3D electromagnetic scattering reconstruction. Once the scattering quantities are reconstructed, scattered fields and RCS can be evaluated for arbitrary observation directions via the radiation integral. U-PINet explicitly learns physics-consistent intermediate scattering representations by modeling local electromagnetic coupling and long-range radiation effects through a hierarchical operator design inspired by near-far field decomposition in fast solvers. A physics-guided graph neural network is incorporated to capture self- and mutual-coupling among mesh elements of complex targets, enabling physically interpretable intermediate representations. By embedding governing equations as residual constraints, U-PINet enables accurate object reconstruction of scattering quantities and consequently reliable RCS prediction across observation directions, while significantly reducing runtime. Extensive numerical experiments demonstrate that U-PINet achieves EM-solver-level RCS accuracy and 3D object reconstruction with orders-of-magnitude speedups, and generalizes well to unseen geometries under limited training data.