标题： 基于物理的神经网络求解电磁逆散射问题 显示英文标题

标题： Physics-Driven Neural Network for Solving Electromagnetic Inverse Scattering Problems

摘要： 近年来，基于深度学习的方法被提出用于解决反散射问题（ISPs），但其中大多数方法严重依赖数据，并且泛化能力有限。 在本文中，提出了一种新的求解方案，该方案通过迭代更新物理驱动神经网络（PDNN）的解，其超参数通过最小化包含从收集的散射场和散射体先验信息约束的损失函数来优化。 与数据驱动的神经网络求解器不同，PDNN仅需要输入收集的散射场和对应预测解的散射场计算，从而避免了泛化问题。 此外，为了加速成像效率，识别了包含散射体的子区域。 数值和实验结果表明，所提出的方案具有高重建精度和强稳定性，即使在处理复合损耗散射体时也是如此。 显示英文摘要

摘要： In recent years, deep learning-based methods have been proposed for solving inverse scattering problems (ISPs), but most of them heavily rely on data and suffer from limited generalization capabilities. In this paper, a new solving scheme is proposed where the solution is iteratively updated following the updating of the physics-driven neural network (PDNN), the hyperparameters of which are optimized by minimizing the loss function which incorporates the constraints from the collected scattered fields and the prior information about scatterers. Unlike data-driven neural network solvers, PDNN is trained only requiring the input of collected scattered fields and the computation of scattered fields corresponding to predicted solutions, thus avoids the generalization problem. Moreover, to accelerate the imaging efficiency, the subregion enclosing the scatterers is identified. Numerical and experimental results demonstrate that the proposed scheme has high reconstruction accuracy and strong stability, even when dealing with composite lossy scatterers.