标题： 可推广的神经电磁逆散射 显示英文标题

标题： Generalizable Neural Electromagnetic Inverse Scattering

摘要： 求解电磁反散射问题（EISP）在医学成像等应用中是基础性的，其目标是从散射的电磁场中重建相对介电常数。 这种逆过程本质上是不适定且高度非线性的，因此特别具有挑战性。 一种基于机器学习的方法 Img-Interiors 通过利用连续隐式函数显示出有前景的结果。 然而，它需要特定案例的优化，缺乏对未见数据的泛化能力，并且在稀疏发射器设置下（例如，仅有一个发射器）会失败。 为了解决这些限制，我们从物理信息的角度重新审视 EISP，将其重新表述为一个两阶段的逆传输-散射过程。 这种表述揭示了感应电流作为可泛化的中间表示，有效地将非线性散射过程与不适定的逆问题解耦。 基于这一见解，我们提出了第一个可泛化的物理驱动框架用于 EISP，包括一个电流估计器和一个介电常数求解器，以端到端的方式工作。 电流估计器显式地学习感应电流作为入射场和散射场之间的物理桥梁，而介电常数求解器则直接从估计的感应电流计算相对介电常数。 这种设计使得在未见数据上能够进行数据驱动的训练和可泛化的前馈预测，同时保持对发射器稀疏性的强鲁棒性。 大量实验表明，我们的方法在重建精度、泛化能力和鲁棒性方面优于最先进的方法。 这项工作为电磁反散射提供了一个根本性的新视角，并代表了向成本效益高的电磁成像实际解决方案迈出的重要一步。 显示英文摘要

摘要： Solving Electromagnetic Inverse Scattering Problems (EISP) is fundamental in applications such as medical imaging, where the goal is to reconstruct the relative permittivity from scattered electromagnetic field. This inverse process is inherently ill-posed and highly nonlinear, making it particularly challenging. A recent machine learning-based approach, Img-Interiors, shows promising results by leveraging continuous implicit functions. However, it requires case-specific optimization, lacks generalization to unseen data, and fails under sparse transmitter setups (e.g., with only one transmitter). To address these limitations, we revisit EISP from a physics-informed perspective, reformulating it as a two stage inverse transmission-scattering process. This formulation reveals the induced current as a generalizable intermediate representation, effectively decoupling the nonlinear scattering process from the ill-posed inverse problem. Built on this insight, we propose the first generalizable physics-driven framework for EISP, comprising a current estimator and a permittivity solver, working in an end-to-end manner. The current estimator explicitly learns the induced current as a physical bridge between the incident and scattered field, while the permittivity solver computes the relative permittivity directly from the estimated induced current. This design enables data-driven training and generalizable feed-forward prediction of relative permittivity on unseen data while maintaining strong robustness to transmitter sparsity. Extensive experiments show that our method outperforms state-of-the-art approaches in reconstruction accuracy, generalization, and robustness. This work offers a fundamentally new perspective on electromagnetic inverse scattering and represents a major step toward cost-effective practical solutions for electromagnetic imaging.