Solving Combined Field Integral Equation With Deep Neural Network for 2-D Conducting Object
Abstract—Solving the combined field integral equation (CFIE) for the large-scale scattering problem is computationally expensive. In this letter, we investigate the feasibility ofapplying deep learning to solve the CFIE for 2-D perfect electrically conducting objects. Inspired by the conjugate gradientmethod, an iterative deep neural network is designed to learn the manner of solving the surface current density from the CFIE, with the input being the coefficient matrix of the equation. This process involves physics through sur- face integration and need less iterations than the conventional iter- ative equation solver. In numerical tests, we evaluate the network’s performance by comparing the predicted surface current density and bistatic scattering cross section with the solutions rigorously computed. This method provides an insight into applying machine learning techniques together with electromagnetic (EM) physics to fast EM computation with the same level of accuracy as traditional method.
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