Electromagnetic Scattering from a Three-dimensional Object using Physics-informed Neural Network

Authors

  • Yuan Zhang School of Physics, Xidian University Xi’an 710071, China
  • Renxian Li School of Physics, Xidian University Xi’an 710071, China
  • Huan Tang School of Physics, Xidian University Xi’an 710071, China
  • Zhuoyuan Shi School of Physics, Xidian University Xi’an 710071, China
  • Bing Wei School of Physics, Xidian University Xi’an 710071, China
  • Shuhong Gong School of Physics, Xidian University Xi’an 710071, China
  • Lixia Yang Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University, China
  • Bing Yan School of Information and Communication Engineering North University of China 030051, China, Shanxi Key Laboratory of Signal Capturing and Processing North University of China, 030051, Shanxi Taiyuan, China

DOI:

https://doi.org/10.13052/2025.ACES.J.400203

Keywords:

electromagnetic scattering, Maxwell’s equations, physics-informed neural network

Abstract

Prediction of electromagnetic fields scattered from objects is of great significance in various fields. Traditional computational electromagnetic solvers, which are mesh-based, are expensive and time-consuming. The deep learning technique becomes an alternative method of the prediction of scattered fields with high efficiency. However, the data-driven deep learning method requires a large data set and lacks robustness. For complicated scattering problems, the construction of a large training data set is a hard task. By considering physics-constraints, physics-informed neural networks (PINNs) can solve the partial differential equation (PDE) problem with a small data set and also provide a physical explanation. In this paper, the PINNs are employed to solve the scattering of a plane wave by a three-dimensional object with Maxwell’s equations being physical constraints. In the calculation, a sphere and an ellipsoid are taken as examples, and the effects of the network parameters (including the number of hidden layers, and the number of data sets) are mainly discussed. The results have practical applications in many fields such as radar detection, biomedical imaging, and satellite navigation.

Downloads

Download data is not yet available.

Author Biographies

Yuan Zhang, School of Physics, Xidian University Xi’an 710071, China

Yuan Zhang received the B.S. degree from Xidian University, Xi’an, China, in 2021. She is currently working toward the M.S. degree in Optics, Xidian University. Her research interests include machine learning, and computational electromagnetism.

Renxian Li, School of Physics, Xidian University Xi’an 710071, China

Renxian Li received the Ph.D. degree from Xidian University in 2008. After graduation, he joined the School of Physics, Xidian University. His research interests includes machine learning, electromagnetic scattering, and computational electromagnetism.

Huan Tang, School of Physics, Xidian University Xi’an 710071, China

Huan Tang received the B.S. degree from Xidian University, Xi’an, China, in 2020. He is currently working toward the Ph.D. degree in Optics, Xidian University. His research interests include neural network, and electromagnetic scattering.

Zhuoyuan Shi, School of Physics, Xidian University Xi’an 710071, China

Zhuoyuan Shi received the B.S. degree from Xidian University, Xi’an, China, in 2022. She is currently working toward the M.S. degree in Optics, Xidian University. Her research interests include machine learning, and electromagnetic scattering.

Bing Wei, School of Physics, Xidian University Xi’an 710071, China

Bing Wei was born in July 1970, graduated from Beijing Normal University in 1993, majoring in Solid State and Ion Beam Physics, and received his Ph.D. degree in Radio Physics from Xidian University of Electronic Science and Technology (XUET) in 2004. He is now a professor of Xidian University of Electronic Science and Technology (XUET), a doctoral supervisor of radio

Shuhong Gong, School of Physics, Xidian University Xi’an 710071, China

Shuhong Gonggong was born in Shanxi, China, in 1978. He received the B.S. degree in physics education from Shanxi Normal University, Xi’an, China, in 2001, the M.S. and Ph.D. degrees in radio physics from Xidian University, Xi’an, in 2004 and 2008, respectively. He is currently a Professor at Xidian University. His research work has been focused on novel antenna design, radio wave propagation, and their applications.

Lixia Yang, Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University, China

Lixia Yangyang was born in Ezhou, Hubei, China, in 1975. He received the B.S. degree in physics from Hubei University, Wuhan, China, in 1997, and the Ph.D. degree in radio physics from Xidian University, Xi’an, China, in 2007.

Since 2010, he has been an Associate Professor with the Communication Engineering Department, Jiangsu University, Zhenjiang, China. From 2010 to 2011, he was a Postdoctoral Research Fellow with the Electro Science Laboratory (ESL), The Ohio State University, Columbus, OH, USA. From 2015 to 2016, he was a Visiting Scholar with the Institute of Space Science, The University of Texas at Dallas, Dallas, TX, USA. From 2016 to 2019, he has been a Professor, a Ph.D. Supervisor, and the Chairman of the Communication Engineering Department, Jiangsu University. Since 2020, he has been a Distinguished Professor, a Ph.D. Supervisor, and the Vice Dean with the School of Electronic and Information Engineering, Anhui University, Hefei, China. His research interests include wireless communication technique, radio sciences, the computational electromagnetic, and the antenna theory and design in wireless communication systems. He is a member of the Editor Board of Radio Science Journal in China.

Bing Yan, School of Information and Communication Engineering North University of China 030051, China, Shanxi Key Laboratory of Signal Capturing and Processing North University of China, 030051, Shanxi Taiyuan, China

Bing Yanyan was born on October 6, 1976. From 2004 to 2009, he studied in the School of Science, Xidian University, and received his doctorate. In recent years, his interesting has been engaged in the research of gas-solid two-phase flow multi-parameter measurement based on light scattering method and electrostatic method.

References

M. S. Zhdanov, Geophysical Inverse Theory and Regularization Problems, vol. 36, Amsterdam: Elsevier, 2002.

A. Abubakar, P. M. Van den Berg, and J. J. Mallorqui, “Imaging of biomedical data using a multiplicative regularized contrast source inversion method,” IEEE Transactions on Microwave Theory and Techniques, vol. 50, no. 7, pp. 1761-1771, 2002.

X. Wang, T. Qin, R. S. Witte, and H. Xin, “Computational feasibility study of contrast-enhanced thermoacoustic imaging for breast cancer detection using realistic numerical breast phantoms,” IEEE Transactions on Microwave Theory and Techniques, vol. 63, no. 5, pp. 1489-1501, 2015.

C. A. Balanis, Antenna Theory: Analysis and Design, New York: Wiley, 2016.

A. Taflove, S. C. Hagness, and M. Piket-May, “Computational electromagnetics: The finite-difference time-domain method,” The Electrical Engineering Handbook, vol. 3, pp. 629-670,2005.

K. Yee, “Numerical solution of initial boundary value problems involving Maxwell’s equations in isotropic media,” IEEE Transactions on Antennas and Propagation, vol. 14, no. 3, pp. 302-307,1966.

J.-M. Jin, The Finite Element Method in Electromagnetics, New York: Wiley, 2015.

R. C. Rumpf, “Simple implementation of arbitrarily shaped total-field/scattered-field regions in finite-difference frequency-domain,” Progress In Electromagnetics Research B, vol. 36, pp. 221-248, 2012.

J. Smajic, C. Hafner, L. Raguin, K. Tavzarashvili, and M. Mishrikey, “Comparison of numerical methods for the analysis of plasmonic structures,” Journal of Computational and Theoretical Nanoscience, vol. 6, no. 3, pp. 763-774, 2009.

A. Massa, D. Marcantonio, X. Chen, M. Li, and M. Salucci, “DNNs as applied to electromagnetics, antennas, and propagation—A review,” IEEE Antennas and Wireless Propagation Letters, vol. 18, no. 11, pp. 2225-2229, 2019.

Y. Li, Y. Wang, S. Qi, Q. Ren, L. Kang, S. D. Campbell, P. L. Werner, and D. H. Werner, “Predicting scattering from complex nano-structures via deep learning,” IEEE Access, vol. 8, pp. 139983-139993, 2020.

I. E. Lagaris, A. Likas, and D. I. Fotiadis, “Artificial neural networks for solving ordinary an partial differential equations,” IEEE Transactions on Neural Networks, vol. 9, no. 5, pp. 987-1000, 1998.

M. Raissi, P. Perdikaris, and G. E. Karniadakis, “Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,” Journal of Computational Physics, vol. 378, pp. 686-707, 2019.

L. D. Landau, The Classical Theory of Fields, vol. 2, Amsterdam: Elsevier, 2013.

Downloads

Published

2025-02-28

How to Cite

[1]
Y. . Zhang, “Electromagnetic Scattering from a Three-dimensional Object using Physics-informed Neural Network”, ACES Journal, vol. 40, no. 02, pp. 103–111, Feb. 2025.

Issue

2025: Vol 40 Iss 2

Section

Novel CEM methods & applications

Categories