Multipass cell design with the random walk and gradient descent optimization algorithms

An automated approach is presented for optimizing the multipass cell (MPC) design with dense patterns in this paper. First, a strategy based on the random walk (RW) algorithm is implemented for global exploration to determine the parameters of the target MPC configuration and accelerate the design p...

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Vydáno v:	Applied physics. B, Lasers and optics Ročník 127; číslo 9
Hlavní autoři:	Kong, Rong, Liu, Peng, Zhou, Xin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2021 Springer Nature B.V
Témata:	Algorithms Applied physics Clustering Configuration management Design optimization Engineering Identification methods Lasers Optical Devices Optics Photonics Physical Chemistry Physics Physics and Astronomy Quantum Optics Random walk Regular Paper
ISSN:	0946-2171, 1432-0649
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Abstract	An automated approach is presented for optimizing the multipass cell (MPC) design with dense patterns in this paper. First, a strategy based on the random walk (RW) algorithm is implemented for global exploration to determine the parameters of the target MPC configuration and accelerate the design process. Second, the gradient descent (GD) algorithm is performed for local exploitation to optimize the re-entrant condition in a fast and automatic way. In addition, we apply the clustering method to identify the desired spot patterns with specific properties automatically. Finally, the proposed algorithms are tested in the optimization of two types of densely patterned MPC under the re-entrant condition. The results presented in this paper clearly show that the proposed approach is effective and efficient in optimizing the MPC design automatically and can be further utilized in more complex optical configurations.
AbstractList	An automated approach is presented for optimizing the multipass cell (MPC) design with dense patterns in this paper. First, a strategy based on the random walk (RW) algorithm is implemented for global exploration to determine the parameters of the target MPC configuration and accelerate the design process. Second, the gradient descent (GD) algorithm is performed for local exploitation to optimize the re-entrant condition in a fast and automatic way. In addition, we apply the clustering method to identify the desired spot patterns with specific properties automatically. Finally, the proposed algorithms are tested in the optimization of two types of densely patterned MPC under the re-entrant condition. The results presented in this paper clearly show that the proposed approach is effective and efficient in optimizing the MPC design automatically and can be further utilized in more complex optical configurations.
ArticleNumber	132
Author	Kong, Rong Liu, Peng Zhou, Xin
Author_xml	– sequence: 1 givenname: Rong surname: Kong fullname: Kong, Rong organization: Center for Advanced Quantum Studies, Applied Optics Beijing Area Major Laboratory, Department of Physics, Beijing Normal University – sequence: 2 givenname: Peng surname: Liu fullname: Liu, Peng organization: Center for Advanced Quantum Studies, Applied Optics Beijing Area Major Laboratory, Department of Physics, Beijing Normal University – sequence: 3 givenname: Xin surname: Zhou fullname: Zhou, Xin email: zhoux@bnu.edu.cn organization: Center for Advanced Quantum Studies, Applied Optics Beijing Area Major Laboratory, Department of Physics, Beijing Normal University
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Chem. doi: 10.1021/acs.analchem.0c01931
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Snippet	An automated approach is presented for optimizing the multipass cell (MPC) design with dense patterns in this paper. First, a strategy based on the random walk...
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SubjectTerms	Algorithms Applied physics Clustering Configuration management Design optimization Engineering Identification methods Lasers Optical Devices Optics Photonics Physical Chemistry Physics Physics and Astronomy Quantum Optics Random walk Regular Paper
Title	Multipass cell design with the random walk and gradient descent optimization algorithms
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