Physics-Data of Hybrid-Driven Edge-Featured Graph Attention Networks for Surface Currents Learning 3-D PEC Targets

To achieve fast and accurate modeling of electrically large targets, this paper proposes a physics-data hybrid driven surface current learning method (PdEgatSCL), which can learn combined field integral operator and achieve end-to-end solving from incident fields to surface currents. The edge-feature graph attention network (EGAT) is employed as the foundational architecture of PdEgatSCL, where the Green’s function and its divergence are explicitly encoded as edge features into the network. The attention mechanism of EGAT is leveraged to model the scattering characteristics of the targets. Additionally, the center coordinates and area of the discretized surface elements are input as node features to ensure that the mesh information is fully utilized. Finally, PdEgatSCL is applied to solve the surface currents of three-dimensional perfect electric conductor (PEC) targets. Numerical results demonstrate that the surface current solutions obtained by PdEgatSCL align well with the ground truth computed by the Method of Moments (MoM). Moreover, compared to SOTA AI-based method, PdEgatSCL exhibits higher accuracy while accelerating the average inference time by 87.67% and reducing memory consumption by 99.24%.