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.

翻译：传统计算电磁学求解器通常先求解三维目标上的感应表面电流，再通过辐射积分计算散射场，从而获得高保真度的雷达散射截面特征。然而，其计算成本在重复查询和大规模三维场景中变得难以承受。近期纯数据驱动网络虽提升了效率，但往往绕过了这一散射机制，可能损害物理一致性与泛化能力。为弥补这一不足，本文提出U-PINet——一种完全端到端的物理信息分层网络，通过三维电磁散射重构实现高效的RCS预测。一旦重构出散射量，即可通过辐射积分计算任意观测方向的散射场与RCS。U-PINet受快速求解器中近远场分解思想启发，通过分层算子设计建模局部电磁耦合与远场辐射效应，从而显式学习物理一致的中间散射表征。网络引入物理引导的图神经网络以捕捉复杂目标网格单元间的自耦合与互耦合，形成具有物理解释性的中间表征。通过将控制方程嵌入残差约束，U-PINet在显著降低运行时间的同时，实现了散射量的精确目标重构及跨观测方向的可靠RCS预测。大量数值实验表明，U-PINet在取得电磁求解器级别RCS精度与三维目标重构效果的同时，实现了数量级的速度提升，并在有限训练数据下对未见几何结构展现出良好泛化能力。