Development of neural networks for modeling diffraction of electromagnetic radiation on a single cylinder and a group of cylindrical objects

We show the effectiveness of using neural networks to model the diffraction of electromagnetic radiation on cylindrical objects, and compare various architectures of neural networks. The error of the neural-network-aided diffraction modeling is evaluated for different problem statements and varying cylinder parameters. The application of the proposed models for the case of several cylinders is also analyzed.