Electromagnetic inverse scattering of a charged spherical particle based on neural network

Huan Tang; Zitong Zhu; Renxian Li; Wenze Zhuang

doi:10.1117/12.2662715

12 April 2023 Electromagnetic inverse scattering of a charged spherical particle based on neural network

Huan Tang, Zitong Zhu, Renxian Li, Wenze Zhuang

Proceedings Volume 12565, Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022); 125652Q (2023) https://doi.org/10.1117/12.2662715
Event: Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022), 2022, Shanghai, China

Abstract

With the rapid development of computational electromagnetics and information science, electromagnetic inverse scattering has attracted more and more attention, and has become one of the most active and cutting-edge research topics in the field of electromagnetics. Traditional inverse scattering schemes are computationally slow, and machine learning has emerged as a relatively new and efficient inverse scattering solution in recent years. This article will build a fully connected neural network for the inverse scattering of a charged sphere illuminated by a plane wave. Parameters such as the charge and radius of spherical particles are used as the input of the neural network. The corresponding far-field scattering intensities are derived by the generalized Lorenz-Mie theory (GLMT). The numerical results of the field intensity corresponding to different scattering angles are sampled as the output of the neural network. Through network parameter adjustment, a forward model is obtained by training. Then take the far-field intensity sampling point as the label as the input to randomly initialize the particle parameters, and realize the inversion of the particle parameters through the stochastic gradient descent algorithm. Compared with the actual value, the relative error is less than 2%, which realizes high-precision real-time inversion of charged spherical particles. This research has important application value in measuring the scattering properties (such as charge, size, etc.) of particles.

Citation Download Citation

Huan Tang, Zitong Zhu, Renxian Li, and Wenze Zhuang "Electromagnetic inverse scattering of a charged spherical particle based on neural network", Proc. SPIE 12565, Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022), 125652Q (12 April 2023); https://doi.org/10.1117/12.2662715

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KEYWORDS

Particles

Neural networks

Inverse scattering

Scattering

Spherical lenses

Electromagnetism

Mie scattering

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