Learning-Based Fast Electromagnetic Scattering Solver Through Generative Adversarial Network

• Published in: IEEE transactions on antennas and propagation Vol. 69; no. 4; pp. 2194 - 2208
• Main Authors: Ma, Zhenchao, Xu, Kuiwen, Song, Rencheng, Wang, Chao-Fu, Chen, Xudong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Electromagnetic (EM) scattering; Electromagnetic scattering; Electromagnetics; forward-induced current learning method (FICLM); generative adversarial network (GAN); Generative adversarial networks; induced currents; Learning systems; Machine learning; Method of moments; method of moments (MoM); Multiplication; Neural networks; Performance evaluation; Permittivity; Wave physics
• ISSN: 0018-926X; 1558-2221
• DOI: 10.1109/TAP.2020.3026447

This article proposes a learning-based noniterative method to solve electromagnetic (EM) scattering problems utilizing pix2pix, a popular generative adversarial network (GAN). Instead of calculating induced currents directly from a matrix inversion, a forward-induced current learning method (FICLM) is introduced to calculate the induced current through a neural-network mapping. The scattered fields can be further calculated through a multiplication of the Green's function with the predicted induced currents. Inspired by wave physics of scattering problems, we have designed three kinds of input schemes, covering different combinations of the given incident field and permittivity contrast, to evaluate the performance of the FICLM model under both single-incidence and multi-incidence cases. Numerical results prove that the proposed FICLM outperforms the method of moments (MoM) in terms of both computational speed and accuracy by use of reference data with a higher precision. The FICLM with the direct sum of permittivity contrast and a so-called Born-type induced current, achieves the best calculation accuracy and generalization capability compared to the other two inputs. The comparison with other types of neural networks, such as U-net, also demonstrates the superior performance of FICLM for dealing with complex scatterers due to the use of adversarial framework in pix2pix. The proposed method paves a new way for the fast solution of EM-scattering problems through deep learning techniques.