Light & Engineering 32 (3) 2024

Volume 32
Date of publication 06/13/2024
Pages 11–19

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Application of Global Optimization for Retrievals from Synthetic Multi-Angle Measurements L&E, Vol.32, No.3, 2024

Articles authors:

Ivan Chuprov, Jiexing Gao, Dmitry S. Efremenko, Feodor Buzaev

Ivan Chuprov, researcher engineer at Moscow Research Centre, Huawei since 2021 and researcher of the laboratory of big data analysis methods at the Higher School of Economics since 2024. His research interests: physics informed neural networks for solving partial differential equations, evolutionary optimization algorithms

Jiexing Gao received the Ph. D. in Math-Phys from Lomonosov Moscow State University, Russia, in 2011. He worked at Fujian Normal University, Fuzhou, China, as an Assistant Professor. From 2014 to 2019, he was with Bell Labs, Shanghai, China, where he was engaged in research on wireless communication, localization, and robot technology. Since the end of 2019, he joined in Huawei Technologies Co., Ltd. He is currently in charge of an optical communication project. His present research interests involve optical fiber communication, math modelling of waveguides, Maxwell equations and numerical methods

Dmitry S. Efremenko, Ph. D. He graduated from the Moscow Power Engineering institute (MPEI) in 2009. He received his Ph. D. degree from the Moscow State University in 2011 and the habilitation degree from MPEI in 2017. Since 2011 he works as a Research Scientist at the German Aerospace Centre (DLR). He is a docent at the Technical University of Munich. He has over 70 peer-reviewed publications. His scientific interests include radiate transfer, remote sensing, and machine learning

Feodor Buzaev graduated from the National Research University MPEI with a degree in Electrical and from the National Research University “Higher School of Economics” with a degree in Applied Mathematics and Informatics. His research interests: neural network approaches for solving physical problems

Abstract:

The retrieval of parameters for a turbid medium presents a challenging and ill-posed inverse problem. In this paper, we investigate the effectiveness of utilizing global optimization algorithms to determine optical properties of the medium such as the optical thickness, the single scattering albedo, the single scattering phase function, and the extinction profile from multi-angle radiance measurements. For this purpose, we consider the application of the Differential Evolution, SHGO, and Dual Annealing solvers.
To address the phase function retrieval problem, we introduce an enhanced modification of Differential Evolution capable of handling this complex task. In the context of phase function retrieval, we find that global optimization solvers demonstrate comparable efficiency when compared to the Gauss-Newton method, which requires the computation of Jacobians. In the case of the extinction profile problem, the incorporation of Jacobian estimation, coupled with Tikhonov regularization, leads to significant enhancements in retrieval accuracy.

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Keywords

DOI: https://doi.org/10.33383/2023–90

Article in RUS:
Svetotekhnika # 3, 2024, pp. 40–46

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