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Machine Learning Pattern Recognition Algorithm With Applications to Coherent Laser Combination

We analyze a new kind of machine learning algorithm designed to feedback stabilize coherently combined lasers. This algorithm learns differential, rather than absolute, values of action in phase space, in order to facilitate learning on initially unstable systems. Experiments have shown that this approach can control small-scale spatial beam combination with high stability. In this paper we analyze the algorithm’s performance and limitations in depth, showing that it can continuously learn during operation in order to track changes. Using simulation, we extend the application to temporal combination, and show that it scales to more complex instances by combining 81 beams.

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Informasi Detail

Judul Seri

IEEE JOURNAL OF QUANTUM ELECTRONICS; Vol.58 No.6 December 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 6100309

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MACHINE LEARNING
NEURAL NETWORK
CONTROL
PATTERN RECOGNITION
PHOTONIC SIGNAL PROCESSING
COHERENT BEAM COMBINATION
PHASE DRIFT

Info Detail Spesifik

DOI: 10.1109/JQE.2022.3204437

Pernyataan Tanggungjawab

Dan Wang, Qiang Du, Tong Zhou, Antonio Gilardi, Mariam Kiran, Bashir Mohammed, Derun Li, Russell Wilcox

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