Improved Machine Learning Algorithms for Optimizing Coherent Pulse Stacking Amplification

Weizhi Du; Weizhi Du; Eunjeong Hyeon; Hanzhang Pei; Hanzhang Pei; Zhengyu Huang; Zhengyu Huang; YeonJoon Cheong; Siyuan Zheng; Almantas Galvanauskas; Almantas Galvanauskas

doi:10.1364/CLEO_AT.2021.JTh3A.1

Conference on Lasers and Electro-Optics
OSA Technical Digest (Optica Publishing Group, 2021),
paper JTh3A.1
•https://doi.org/10.1364/CLEO_AT.2021.JTh3A.1

Improved Machine Learning Algorithms for Optimizing Coherent Pulse Stacking Amplification

Weizhi Du, Eunjeong Hyeon, Hanzhang Pei, Zhengyu Huang, YeonJoon Cheong, Siyuan Zheng, and Almantas Galvanauskas

CLEO: Science and Innovations 2021

San Jose, California United States
9–14 May 2021
ISBN: 978-1-943580-91-0

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Joint Poster Session III (JTh3A)

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W. Du, E. Hyeon, H. Pei, Z. Huang, Y. Cheong, S. Zheng, and A. Galvanauskas, "Improved Machine Learning Algorithms for Optimizing Coherent Pulse Stacking Amplification," in Conference on Lasers and Electro-Optics, J. Kang, S. Tomasulo, I. Ilev, D. Müller, N. Litchinitser, S. Polyakov, V. Podolskiy, J. Nunn, C. Dorrer, T. Fortier, Q. Gan, and C. Saraceno, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper JTh3A.1.

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Abstract

We apply momentum stochastic parallel gradient descent (MSPGD) and policy gradient algorithms to optimize coherent pulse stacking (CPS), and demonstrate their increased effectiveness compared to traditionally used stochastic parallel gradient descent (SPGD) algorithm.

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