# Application of Machine Learning in Electromagnetics: Mini-Review
## 3 ML and EM
### Inverse Scattering Problem  
The electromagnetic inverse scattering problems (ISPs) seek to determine the nature of the unknown (i.e., location, shape, and constitutive parameters) from the knowledge of measured scattered fields. The imaging techniques based on ISPs are instrumental in numerous areas, such as remote sensing, biomedical imaging, through-wall imaging, geophysics, and non-destructive evaluation. ISPs are challenging to solve due to their intrinsic nonlinearity. This has led to the development of many various EM inverse scattering methods, which can be categorized into two groups: (1) Deterministic optimization methods including the subspace optimization method (SOM) [112], distorted born iterative method [113], and contrast source inversion (CSI) [114], and (2) stochastic methods [115] including genetic algorithms, evolutionary optimization, and PSO algorithms. Compressive-sensing (CS)-based methods are used as effective regularization tools to mitigate the ISP-related challenges, especially for high-contrast and large objects, due to the high complexity and long computing time. DNNs have been successfully used to solve inverse problems. Moreover, the learning-by-examples (LDEs) methods have been proposed to devise various ML approaches to resolve the ISPs [116]. [PERSON] et al. [20] developed DL schemes for ISP that were able to produce good quantitative results by training a U-Net [117]; a unique CNNs architecture originally designed for bio-medical segmentation.  
To exploit the relationship between the DNNs architecture and the iterative methods of nonlinear EM inverse scattering, the author [20] proposed three inversion schemes based on a U-Net CNN.  
[PERSON] et al. [118] investigated a Deep Neural Network for Nonlinear Inverse Electromagnetic Scattering (Deep-NIS), which was based on a cascade of three CNN modules, in which the inputs from the scene were processed by the backpropagation (BP) method and the output was the reconstruction images of the unknown scatterers. Deep-learning schemes could achieve excellent performance, but they are too difficult to implement using the physical knowledge of electromagnetic inverse scattering. Thus, to bridge the gap between physical knowledge and learning approaches, [PERSON] et al. [119] proposed an induced current-learning method by integrating benefits in the iterative algorithm architecture of CNN. To solve the ISPs with high contrast, a contrast source network (CS-Net) combined with a traditional subspace-based optimization method was investigated [120] and CNNs were developed with three stages [121]. Furthermore, [PERSON] et al. [122] proposed a two-step deep-learning approach that can reproduce high-contrast objects using a cascade of CNNs and another complex-valued deep residual CNN. In another study [123], a gradient learning approach was used to invert transient electromagnetic data. Thus, the inversion methods using only phase-less data (e.g., only amplitude data) are preferred for engineering applications. [PERSON] et al. [124] proposed three inversion schemes based on U-net CNNs to solve phase-less ISPs.