Multi-task diffusion graph model for aero-electromagnetic analysis of blended-wing-body configurations

Blended-wing-body configurations require coordinated assessment of aerodynamic loading and electromagnetic scattering characteristics; however, high-fidelity computational fluid dynamics and computational electromagnetics solvers remain too expensive for large-scale computations. We propose a multi-task diffusion graph neural network (GNN) defined on unstructured surface meshes that jointly predicts the surface pressure-coefficient field C_p, the global aerodynamic coefficients (C_L, C_D), and mean monostatic radar cross section (RCS) levels under horizontal and vertical polarizations (R_H, R_V). The node field and global scalars are concatenated into a joint diffusion state and denoised by a hierarchical, mesh-aware graph neural network; physical consistency between C_p and (C_L, C_D) is encouraged by a surface-integral regularizer, and task-appropriate conditioning is enforced through hard-gated masking for the mean-RCS branch. On a dataset of 3800 geometries evaluated at Mach numbers 0.60, 0.85, and 1.50 and angles of attack 2 ° and 6 °, the model attains R ² = 0.969 for C p, mean absolute errors (MAEs) of 0.0065/0.0013 for C_L / C_D, and MAEs of 0.381/0.358 dB for (R_H, R_V) on a geometry-disjoint test set. The reported errors should therefore be interpreted as solver-level surrogate fidelity with respect to the label-generation pipelines, rather than as independent experimental validation. In the long-step setting, inference provides an approximately 35 × wall-clock speedup for the coupled aerodynamic–electromagnetic outputs relative to the solver pipelines used for label generation. In a short-step setting focused on C_p, the full pressure field is produced in 2.705 s per sample.