Physics-Driven Neural Network for Solving Electromagnetic Inverse Scattering Problems
This addresses the generalization problem in inverse scattering for applications like medical imaging or non-destructive testing, though it is an incremental improvement over existing neural network methods.
The paper tackles the limited generalization of data-driven deep learning methods for electromagnetic inverse scattering problems by proposing a physics-driven neural network (PDNN) that iteratively updates solutions using constraints from scattered fields and prior information, achieving high reconstruction accuracy and strong stability for composite lossy scatterers.
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.