Explainable Diffusion Model for Aperture-flexible 3D Quantitative Electromagnetic Imaging.

Three-dimensional quantitative electromagnetic imaging is essential for accurately characterizing concealed or geometrically complex targets. Existing approaches, however, predominantly rely on point-based representations or 2D reconstruction paradigms, which often require strong target priors, neglect 3D geometric dependencies, and inadequately incorporate the physics of electromagnetic scattering. In this work, we propose EDMA, a physics-informed diffusion framework that directly reconstructs complete 3D meshes together with their associated constitutive parameters from measured electromagnetic fields. Built upon a conditional scatter-to-mesh diffusion formulation, EDMA integrates current-consistency constraints, realized through a lightweight wavelet-transform-enhanced ResMLP for efficient induced-current prediction, with an aperture-adaptive encoding mechanism for robust operation under diverse and incomplete measurement configurations. By explicitly enforcing Maxwell-consistent coupling among the scattered field, contrast function, and induced current, EDMA provides a physically interpretable reconstruction pathway in which the generated object is constrained and verifiable through electromagnetic consistency rather than solely by data-driven fitting. Extensive experiments on multiple 3D benchmark datasets and realistic electromagnetic scenarios demonstrate that EDMA achieves superior reconstruction quality while maintaining efficient single-step inference compared with state-of-the-art methods. Moreover, EDMA exhibits strong generalization and robustness under limited-aperture measurement conditions, making it a promising solution for practical 3D quantitative electromagnetic imaging applications.