用物理约束神经网络解决电磁逆散射，无需大量数据即可高精度成像。
Physics-Driven Neural Network for Solving Electromagnetic Inverse Scattering Problems
- 基于物理规律的神经网络迭代更新，仅需散射场输入
- 对复合损耗目标重建准确率高，实验验证稳定
- 适合缺乏训练数据的复杂电磁成像场景
近年来，基于深度学习的反散射方法被提出用于求解逆散射问题（ISPs），但多数依赖大量数据且泛化能力有限。本文提出一种新求解方案：通过物理驱动神经网络（PDNN）迭代更新解，其超参数通过最小化包含散射场约束和散射体先验信息的损失函数优化。与数据驱动方法不同，PDNN仅需输入采集到的散射场并计算预测解对应的散射场即可训练，避免了泛化问题。此外，为提升成像效率，识别出包含散射体的子区域。数值与实验结果表明，该方案在处理复合损耗散射体时仍具有高重建精度和强稳定性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。