Fast Electromagnetic Inversion Solver Based on Conditional Generative Adversarial Network for High-Contrast and Heterogeneous Scatterers

This paper proposes a novel fast electromagnetic (EM) inversion solver for heterogeneous scatterers with high contrast. In order to ensure inversion accuracy, the conventional solvers for the electromagnetic inverse scattering (EMIS) problems usually require EM scattered field information originated from multiple incident EM waves, resulting in tedious measurement operation and considerable measurement data for inversion computation. Thus, the conventional solvers are difficult to be suitable for real-time quantitative EM problems, which requires relatively simple measurement operation and small measurement data dimension. To overcome these challenges, a novel EM inversion solver is proposed based on conditional deep convolutional generative adversarial network (CDCGAN). The proposed CDCGAN includes the generator with an EM scattering simulator and the corresponding discriminator, both consisting of the complex-valued deep convolutional neural networks (DConvNets). The trained CDCGAN can be successfully applied to inhomogeneous and high-contrast scatterers only by employing one single incident EM wave in single-frequency and far-field measurement, which greatly simplifies measurement and reduces the computation complexity for its further inversion computation. Numerical examples have indicated the accuracy and feasibility of the proposed DL-based inversion solver, which acts as the potential candidate for solving real-time quantitative EM problems.