U-PINet: Physics-Informed Hierarchical Learning for Accurate and Fast 3D RCS Prediction

Abstract

Accurate radar cross section (RCS) computation is a fundamental task in radar engineering and electromagnetic (EM) scattering analysis, underpinning target signature characterization, detection, and recognition. Conventional computational electromagnetics (CEM) solvers provide high-fidelity RCS predictions but suffer from prohibitive computational costs when applied to 3-dimensional (3D) targets under multi-aspect configurations. In contrast, purely data-driven neural networks offer high efficiency yet often lack physical consistency and generalization capability. To address these challenges, this paper proposes a U-shaped Physics-Informed Network (U-PINet). To the best of our knowledge, it is the first framework to establish a fully end-to-end, physics-informed hierarchical architecture for fast and accurate RCS computation, grounded in the governing principles of CEM. Inspired by the near-far field decomposition in classical fast solvers, U-PINet explicitly models local EM coupling and long-range radiation effects through a hierarchical operator design. A physics-guided graph construction is further introduced to represent self- and mutual-coupling among mesh elements of complex 3D targets, enabling physically interpretable intermediate representations. By embedding EM governing equations as residual constraints, the proposed framework achieves end-to-end, physically consistent RCS prediction with significantly improved computational efficiency. Extensive numerical experiments demonstrate that U-PINet attains solver-level RCS accuracy with orders-of-magnitude runtime reduction, while exhibiting strong generalization to unseen target geometries under limited training data.