volume | PIER Journals

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, σ_ext, 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 σ_ext predicted against that produced by the original GMM code. The σ_ext 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 σ_ext in place of the original GMM code at a much cheaper cost, albeit with a slight penalty in terms of absolute accuracy.

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Dr. Ying Li Thong

Dr. Tiem Leong Yoon