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20 January 2023 Calculation of the pulse performance in linear cavity fiber mode-locked based on artificial neural network
Xuexiao Ma, Jiaqiang Lin, Chuansheng Dai, Lixin Xu, Peijun Yao
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Proceedings Volume 12563, AOPC 2022: AI in Optics and Photonics; 1256303 (2023) https://doi.org/10.1117/12.2651366
Event: Applied Optics and Photonics China 2022 (AOPC2022), 2022, Beijing, China
Abstract
In this work, we propose to utilize the artificial neural network (ANN) to realize the computing of the pulse performance of the linear cavity fiber laser. At the first, a four hidden layer ANN (called ANN1) is trained to judge whether a small noise pulse in the fiber cavity can evolve into a stable mode-locked pulse with different cavity parameters. ANN1 has an accuracy of 98.3% on the test data set and we use it to quickly calculate the pulse convergence region in the three-dimensional parameter space. Then, a three hidden layer ANN (called ANN2) is trained to calculate the output pulses shape of fiber laser, and its accuracy is verified. After that, based on ANN2 and genetic algorithm, we design a method to inverse deducing the laser parameters with known output pulse width. This algorithm has a small-time complexity. By repeating the genetic process, the accuracy of this algorithm will also be improved. The authors believe that the neural network model presented in this work is an efficient and universal means to study the dynamics of optical fibers and will have a great application prospect in future related work.
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Xuexiao Ma, Jiaqiang Lin, Chuansheng Dai, Lixin Xu, and Peijun Yao "Calculation of the pulse performance in linear cavity fiber mode-locked based on artificial neural network", Proc. SPIE 12563, AOPC 2022: AI in Optics and Photonics, 1256303 (20 January 2023); https://doi.org/10.1117/12.2651366
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KEYWORDS
Mode locking
Fiber lasers
Genetic algorithms
Artificial neural networks
Machine learning
Numerical analysis
Optical simulations
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