Machine learning-based pulse characterization in figure-eight mode-locked lasers

Kokhanovskiy, Alexey; Bednyakova, Anastasia; Kuprikov, Evgeny; Ivanenko, Aleksey; Dyatlov, M. K.; Lotkov, Daniil; Kobtsev, Sergey; Turitsyn, Sergei K.

doi:10.1364/ol.44.003410

Cited by 29 publications

(7 citation statements)

References 12 publications

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“…[9,10] In laser optics, AI techniques substantially enhance the automatic optimization of mode-locked lasers [11][12][13][14][15][16][17][18][19][20][21] and the deep learning-based pulse characterization reveals outstanding robustness against noise. [22,23] The modulation instabilities in optical fiber are analyzed by a fully-connected neural network [24] and precise phase retrieval in the interferometric fringe patterns is also achieved by AI models. [25,26] On the other hand, with GPU-enabled compute unified device architecture (CUDA), the feed-forward computation of AI models can be substantially accelerated by parallel computing.…”

Section: Doi: 101002/lpor202200363mentioning

confidence: 99%

Fast Predicting the Complex Nonlinear Dynamics of Mode‐Locked Fiber Laser by a Recurrent Neural Network with Prior Information Feeding

Liu

Yang

et al. 2023

Laser & Photonics Reviews

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As an imperative method of investigating the internal mechanism of femtosecond lasers, traditional femtosecond laser modeling relies on the split‐step Fourier method (SSFM) to iteratively resolve the nonlinear Schrödinger equation suffering from the large computation complexity. To realize inverse design and optimization of femtosecond lasers, numerous simulations of mode‐locked fiber lasers with different cavity settings are required, further highlighting the time‐consuming problem induced by the large computation complexity. Here, a recurrent neural network is proposed to realize fast and accurate femtosecond mode‐locked fiber laser modeling. The generalization over different cavity settings is achieved via the proposed prior information feeding method. With the acceleration of GPU, the mean time of the artificial intelligence (AI) model inferring 500 roundtrips is less than 0.1 s, which is ≈146 times faster than the SSFM running on a CPU. The proposed AI‐enabled method is promising to become a standard approach to femtosecond laser modeling.

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Section: Doi: 101002/lpor202200363mentioning

confidence: 99%

Fast Predicting the Complex Nonlinear Dynamics of Mode‐Locked Fiber Laser by a Recurrent Neural Network with Prior Information Feeding

Liu

Yang

et al. 2023

Laser & Photonics Reviews

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“…One research in the fiber laser field showed temporal duration characterization of the mode-locked pulses using the dispersive Fourier transform trace [115]. The trained artificial neural network can predict the pulse duration with an average consistency of 95%.…”

Section: Ultrashort Pulses Reconstructionmentioning

confidence: 99%

Fiber laser development enabled by machine learning: review and prospect

Jiang

et al. 2022

PhotoniX

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In recent years, machine learning, especially various deep neural networks, as an emerging technique for data analysis and processing, has brought novel insights into the development of fiber lasers, in particular complex, dynamical, or disturbance-sensitive fiber laser systems. This paper highlights recent attractive research that adopted machine learning in the fiber laser field, including design and manipulation for on-demand laser output, prediction and control of nonlinear effects, reconstruction and evaluation of laser properties, as well as robust control for lasers and laser systems. We also comment on the challenges and potential future development.

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“…Ultrafast lasers are also stochastic systems and the impact of noise can generally be only reproduced via computationally intensive Monte-Carlo simulations that require the analysis of a very large amount of data. One can anticipate that the use of machine learning techniques for pattern recognition combined with the latest advances in real-time measurement techniques [40,41] could lead to better understanding of ultrafast laser dynamics, allowing for the construction of laser systems with improved robustness.…”

Section: Self-tuning Of Ultrafast Fibre Lasersmentioning

confidence: 99%

Machine learning and applications in ultrafast photonics

et al. 2020

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Recent years have seen the rapid growth and development of the field of smart photonics, where machine learning algorithms are being matched to optical systems to add new functionalities and to enhance performance. An area where machine learning shows particular potential to accelerate technology is the field of ultrafast photonics -the generation and characterization of light pulses, the study of light-matter interactions on short timescales, and high-speed optical measurements. Our aim here is to highlight a number of specific areas where the promise of machine learning in ultrafast photonics has already been realized, including the design and operation of pulsed lasers, and the characterization and control of ultrafast propagation dynamics. We also consider challenges and future areas of research.

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Machine learning-based pulse characterization in figure-eight mode-locked lasers

Cited by 29 publications

References 12 publications

Fast Predicting the Complex Nonlinear Dynamics of Mode‐Locked Fiber Laser by a Recurrent Neural Network with Prior Information Feeding

Fast Predicting the Complex Nonlinear Dynamics of Mode‐Locked Fiber Laser by a Recurrent Neural Network with Prior Information Feeding

Fiber laser development enabled by machine learning: review and prospect

Machine learning and applications in ultrafast photonics

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