Vol. 112

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2022-07-24

A Neural Network Representation of Generalized Multiparticle Mie-Solution

By Ying Li Thong and Tiem Leong Yoon
Progress In Electromagnetics Research M, Vol. 112, 15-28, 2022
doi:10.2528/PIERM22050504

Abstract

Generalized Lorentz-Mie Theory (GLMT) provides analytical far-field solutions to electromagnetic (EM) scattering of an aggregate of spheres in a fixed orientation. One of the computational codes that implements the GLMT calculation is that provided by Xu, dubbed GMM which returns EM responses such as the extinction cross section, σext, given the information of incident wavelength, particle arrangement, the common radius, and reflective indices of the aggregate. We have attempted to represent the GMM code in the form a neural network dubbed NNGMM. The NNGMM obtained was stress tested and systematically quantified for its accuracy by comparing the σext predicted against that produced by the original GMM code. The σext produced by the NNGMM for arbitrary aggregates at random wavelength yielded a good fidelity with respect to that calculated by the GMM calculator up to an R-squared value of above 99% level and mean squared error of ≈5.0. The realization of NNGMM proves the feasibility of representing the GMM code by a neural network. The optimally-performing NNGMM obtained in this work can serve as an alternative computational tool for calculating σext in place of the original GMM code at a much cheaper cost, albeit with a slight penalty in terms of absolute accuracy.

Citation


Ying Li Thong and Tiem Leong Yoon, "A Neural Network Representation of Generalized Multiparticle Mie-Solution," Progress In Electromagnetics Research M, Vol. 112, 15-28, 2022.
doi:10.2528/PIERM22050504
http://jpier.org/PIERM/pier.php?paper=22050504

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