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Min Jiang, Hanshuo Wu, Yi An, Tianyue Hou, Qi Chang, Liangjin Huang, Jun Li, Rongtao Su, Pu Zhou. Fiber laser development enabled by machine learning: review and prospect[J]. PhotoniX. doi: 10.1186/s43074-022-00055-3
Citation: Min Jiang, Hanshuo Wu, Yi An, Tianyue Hou, Qi Chang, Liangjin Huang, Jun Li, Rongtao Su, Pu Zhou. Fiber laser development enabled by machine learning: review and prospect[J]. PhotoniX. doi: 10.1186/s43074-022-00055-3

doi: 10.1186/s43074-022-00055-3

Fiber laser development enabled by machine learning: review and prospect

Funds: This work is supported by Projects for National Excellent Young Talents and Hunan Provincial Innovation Construct Project (No. 2019RS3017).
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  • [1] Fermann ME, Hartl I. Ultrafast fiber laser technology. IEEE J Select Topics Quantum Electron. 2009;15(1):191–204. https://doi.org/10.1109/JSTQE.2008.2010246.
    [2] Fermann ME, Hartl I. Ultrafast fibre lasers. Nat Photonics. 2013;7(11):868–74. https://doi.org/10.1038/nphoton.2013.280.
    [3] Zervas MN, Codemard CA. High power fiber lasers: A review. IEEE J Select Topics Quantum Electron. 2014;20(5):219–41. https://doi.org/10.1109/JSTQE.2014.2321279.
    [4] Jauregui C, Limpert J, Tünnermann A. High-power fibre lasers. Nat Photonics. 2013;7(11):861–7. https://doi.org/10.1038/nphoton.2013.273.
    [5] Liu Z, et al. High-power coherent beam polarization combination of fiber lasers: progress and prospect [Invited]. J Opt Soc Am B. 2017;34(3):A7. https://doi.org/10.1364/josab.34.0000a7.
    [6] Xu C, Wise FW. Recent advances in fibre lasers for nonlinear microscopy. Nat Photonics. 2013;7(11):875–82. https://doi.org/10.1038/nphoton.2013.284.
    [7] Kapron FP, Keck DB. Pulse Transmission Through a Dielectric Optical Waveguide. Appl Opt. 1971;10(7):1519. https://doi.org/10.1364/ao.10.001519.
    [8] Li T. Optical Fibers for Communications. Opt News. 1977;3(3):10–5. https://doi.org/10.1364/on.3.2.000010.
    [9] Olsen FO, Hansen KS, Nielsen JS. Multibeam fiber laser cutting. J Laser Appl. 2009;21(3):133–8. https://doi.org/10.2351/1.3184436.
    [10] Yang J, Tang Y, Xu J. Development and applications of gain-switched fiber lasers [Invited]. Photonics Res. 2013;1(1):52. https://doi.org/10.1364/prj.1.000052.
    [11] Churkin DV, et al. Recent advances in fundamentals and applications of random fiber lasers. Adv Opt Photon. 2015;7(3):516. https://doi.org/10.1364/aop.7.000516.
    [12] Fu S, et al. Review of recent progress on single-frequency fiber lasers. J Opt Soc Am B. 2017;34(3):A49. https://doi.org/10.1364/josab.34.000a49.
    [13] Shang C, et al. Review on wavelength-tunable pulsed fiber lasers based on 2D materials. Opt Laser Technol. 2020;131(September 2019). https://doi.org/10.1016/j.optlastec.2020.106375.
    [14] Dragic PD, Cavillon M, Ballato J. Materials for optical fiber lasers: A review. Appl Phys Rev. 2018;5(4). https://doi.org/10.1063/1.5048410.
    [15] Samuel AL. Some studies in machine learning using the game of checkers. IBM J Res Dev. 2000;44(1–2):207–19. https://doi.org/10.1147/rd.441.0206.
    [16] De Santana LMQ, et al. Deep Neural Networks for Acoustic Modeling in the Presence of Noise. IEEE Lat Am Trans. 2018;16(3):918–25. https://doi.org/10.1109/TLA.2018.8358674.
    [17] Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84–90. https://doi.org/10.1145/3065386.
    [18] Jiao Z, et al. Machine learning and deep learning in chemical health and safety: A systematic review of techniques and applications. J Chem Health Saf. 2020;27(6):316–34. https://doi.org/10.1021/acs.chas.0c00075.
    [19] Ongie G, et al. Deep Learning Techniques for Inverse Problems in Imaging. IEEE J Select Areas Inform Theory. 2020;1(1):39–56. https://doi.org/10.1109/jsait.2020.2991563.
    [20] Barbastathis G, Ozcan A, Situ G. On the use of deep learning for computational imaging. Optica. 2019;6(8):921. https://doi.org/10.1364/optica.6.000921.
    [21] Zhao R, Huang L, Wang Y. Recent advances in multi-dimensional metasurfaces holographic technologies. PhotoniX. 2020;1(1):1–24. https://doi.org/10.1186/s43074-020-00020-y.
    [22] Zuo C, et al. Deep learning in optical metrology: a review. Light Sci Appl. 2022;11(1). https://doi.org/10.1038/s41377-022-00714-x.
    [23] Musumeci F, et al. An Overview on Application of Machine Learning Techniques in Optical Networks. IEEE Commun Surv Tutorials. 2019;21(2):1383–408. https://doi.org/10.1109/COMST.2018.2880039.
    [24] Wang D, et al. Data-driven Optical Fiber Channel Modeling: A Deep Learning Approach. J Lightwave Technol. 2020;38(17):4730–43. https://doi.org/10.1109/JLT.2020.2993271.
    [25] Zhang Y, et al. Ultrafast and Accurate Temperature Extraction via Kernel Extreme Learning Machine for BOTDA Sensors. J Lightwave Technol. 2021;39(5):1537–43. https://doi.org/10.1109/JLT.2020.3035810.
    [26] Ma W, et al. Deep learning for the design of photonic structures. Nat Photonics. 2021;15(2):77–90. https://doi.org/10.1038/s41566-020-0685-y.
    [27] Wiecha PR, et al. Deep learning in nano-photonics: inverse design and beyond. Photonics Res. 2021;9(5):B182. https://doi.org/10.1364/prj.415960.
    [28] Malkiel I, et al. Plasmonic nanostructure design and characterization via Deep Learning. Light Sci Appl. 2018;7(1). https://doi.org/10.1038/s41377-018-0060-7.
    [29] Situ G, Westbrook P. AI boosts photonics and vice versa AI boosts photonics and vice versa: AIP Publishing, LLC; 2020. https://doi.org/10.1063/5.0017902.
    [30] Woodward RI, Kelleher EJR. Genetic algorithm-based control of birefringent filtering for self-tuning, self-pulsing fiber lasers. Opt Lett. 2017;42(15):2952. https://doi.org/10.1364/ol.42.002952.
    [31] Wu X, et al. Intelligent Breathing Soliton Generation in Ultrafast Fiber Lasers. Laser Photonics Rev. 2022;16(2):2100191. https://doi.org/10.1002/lpor.202100191.
    [32] Nathan Kutz J, Fu X, Brunton S. Self-tuning fiber lasers: Machine learning applied to optical systems. Nonlinear Photonics. 2014;2014:1–2. https://doi.org/10.1364/np.2014.ntu4a.7.
    [33] Mitchell TM. Machine Learning. New York: McGraw-Hill; 1997.
    [34] Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks. In: 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings; 2016. p. 1–16.
    [35] Tamir JI, Yu SX, Lustig M. Unsupervised Deep Basis Pursuit: Learning inverse problems without ground-truth data; 2019. p. 1–5.
    [36] van Engelen JE, Hoos HH. A survey on semi-supervised learning. Mach Learn. 2020;109(2):373–440. https://doi.org/10.1007/s10994-019-05855-6.
    [37] Nilsson NJ. Introduction to Machine Learning. An early draft of a proposed textbook. Mach Learn. 2005;56(2):387–99 10.1.1.167.8023.
    [38] Shalev-Shwartz S, Ben-David S. Understanding Machine Learning, in Understanding Machine Learning: From Theory to Algorithms 9781107057. Cambridge: Cambridge University Press; 2014. https://doi.org/10.1017/CBO9781107298019.
    [39] Qiu J, et al. A survey of machine learning for big data processing. Eurasip J Adv Signal Process. 2016;(1). https://doi.org/10.1186/s13634-016-0355-x.
    [40] Martin E, et al. Semi-Supervised Learning. In: Encyclopedia of Machine Learning. Boston: Springer; 2011. p. 892–7. https://doi.org/10.1007/978-0-387-30164-8_749.
    [41] Morales EF, Zaragoza JH. An introduction to reinforcement learning. In: Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions; 2011. p. 63–80. https://doi.org/10.4018/978-1-60960-165-2.ch004.
    [42] Nousiainen J, et al. Adaptive optics control using model-based reinforcement learning. Opt Express. 2021;29(10):15327. https://doi.org/10.1364/oe.420270.
    [43] Brereton RG, Lloyd GR. Support Vector Machines for classification and regression. Analyst. 2010;135(2):230–67. https://doi.org/10.1039/b918972f.
    [44] Bo D, et al. Structural Deep Clustering Network. In: The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020; 2020. p. 1400–10. https://doi.org/10.1145/3366423.3380214.
    [45] Min E, et al. A Survey of Clustering with Deep Learning: From the Perspective of Network Architecture. IEEE Access. 2018;6(July):39501–14. https://doi.org/10.1109/ACCESS.2018.2855437.
    [46] LeCun Y, et al. Backpropagation Applied to Handwritten Zip Code Recognition. Neural Comput. 1989;1(4):541–51. https://doi.org/10.1162/neco.1989.1.4.541.
    [47] Lecun Y, et al. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278–324. https://doi.org/10.1109/5.726791.
    [48] Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings; 2017. p. 1–14.
    [49] Solomatine D, See LM, Abrahart RJ. Data-Driven Modelling: Concepts, Approaches and Experiences. Pract Hydroinf. 2008:17–30. https://doi.org/10.1007/978-3-540-79881-1_2.
    [50] Karniadakis GE, et al. Physics-informed machine learning. Nat Rev Physics. 2021;3(6):422–40. https://doi.org/10.1038/s42254-021-00314-5.
    [51] Raissi M, Perdikaris P, Karniadakis GE. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys. 2019;378(October):686–707. https://doi.org/10.1016/j.jcp.2018.10.045.
    [52] Raissi M. Deep hidden physics models: Deep learning of nonlinear partial differential equations. J Mach Learn Res. 2018;19:1–24.
    [53] Brunton SL, et al. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc Natl Acad Sci U S A. 2016;113(15):3932–7. https://doi.org/10.1073/pnas.1517384113.
    [54] Bengio Y, Courville A, Vincent P. Representation Learning: A Review and New Perspectives. IEEE Trans Pattern Anal Mach Intell. 2013;35(8):1798–828. https://doi.org/10.1109/TPAMI.2013.50.
    [55] Yu D, et al. Deep learning and its applications to signal and information processing. IEEE Signal Process Mag. 2011;28(1):145–50. https://doi.org/10.1109/MSP.2010.939038.
    [56] Lecun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44. https://doi.org/10.1038/nature14539.
    [57] McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys. 1943;5(4):115–33. https://doi.org/10.1007/BF02478259.
    [58] Salehinejad H, et al. Recent Advances in Recurrent Neural Networks; 2017. p. 1–21.
    [59] Bennett KP, Parrado-Hernández E. The interplay of optimization and machine learning research. J Mach Learn Res. 2006;7:1265–81. https://doi.org/10.5555/1248547.
    [60] Zhang J, et al. Why gradient clipping accelerates training: A theoretical justification for adaptivity; 2019. p. 1–21.
    [61] Wilson AC, et al. The marginal value of adaptive gradient methods in machine learning. Adv Neural Inf Proces Syst. 2017;(Nips):4149–59. http://arxiv.org/abs/1705.08292.
    [62] Ruder S. An overview of gradient descent optimization algorithms. In: arXiv preprint arXiv:160904747; 2016. p. 1–14. http://arxiv.org/abs/1609.04747.
    [63] Yao X. Evolving artificial neural networks. Proc IEEE. 1999;87(9):1423–47. https://doi.org/10.1109/5.784219.
    [64] F. P. Such et al., “Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning” (2017).
    [65] Conti E, et al. Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents. Adv Neural Inf Proces Syst. 2018;(NeurIPS):5027–38. http://arxiv.org/abs/1712.06560.
    [66] Rere LMR, Fanany MI, Arymurthy AM. Simulated Annealing Algorithm for Deep Learning. Procedia Comput Sci. 2015;72:137–44. https://doi.org/10.1016/j.procs.2015.12.114.
    [67] Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science. 2006;313(5786):504–7. https://doi.org/10.1126/science.1127647.
    [68] Wang H, Czerminski R, Jamieson AC. Neural Networks and Deep Learning. In: The Machine Age of Customer Insight; 2021. p. 91–101. https://doi.org/10.1108/978-1-83909-694-520211010.
    [69] Mnih V, et al. Playing Atari with Deep Reinforcement Learning. In: Deep Reinforcement Learning; 2013. p. 135–60.
    [70] Vlachas PR, et al. Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics. Neural Netw. 2020;126:191–217. https://doi.org/10.1016/j.neunet.2020.02.016.
    [71] Pandey S, Schumacher J. Reservoir computing model of two-dimensional turbulent convection. Phys Rev Fluids. 2020;5(11):113506. https://doi.org/10.1103/PhysRevFluids.5.113506.
    [72] Vlachas PR, et al. Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks. (arXiv:1802.07486v4 [physics.comp-ph] UPDATED). Phys Today. 2018. https://doi.org/10.1098/rspa.2017.0844.
    [73] Salmela L, et al. Predicting ultrafast nonlinear dynamics in fibre optics with a recurrent neural network. Nat Machine Intell. 2021;3(4):344–54. https://doi.org/10.1038/s42256-021-00297-z.
    [74] Teğin U, et al. Reusability report: Predicting spatiotemporal nonlinear dynamics in multimode fibre optics with a recurrent neural network. Nat Machine Intell. 2021;3(5):387–91. https://doi.org/10.1038/s42256-021-00347-6.
    [75] Sui H, et al. Deep learning based pulse prediction of nonlinear dynamics in fiber optics. Opt Express. 2021;29(26):44080. https://doi.org/10.1364/oe.443279.
    [76] Lim J, Psaltis D. MaxwellNet: Physics-driven deep neural network training based on Maxwell’s equations. APL Photonics. 2022;7(1):011301. https://doi.org/10.1063/5.0071616.
    [77] Tünnermann H, Shirakawa A. Deep reinforcement learning for coherent beam combining applications. Opt Express. 2019;27(17):24223. https://doi.org/10.1364/oe.27.024223.
    [78] Tünnermann H, Shirakawa A. Deep reinforcement learning for tiled aperture beam combining in a simulated environment. JPhys Photonics. 2021;3(1). https://doi.org/10.1088/2515-7647/abcd83.
    [79] Chen J, Jiang H. Optimal Design of Gain-Flattened Raman Fiber Amplifiers Using a Hybrid Approach Combining Randomized Neural Networks and Differential Evolution Algorithm. IEEE Photonics J. 2018;10(2). https://doi.org/10.1109/JPHOT.2018.2817843.
    [80] Hou T, et al. Deep-learning-assisted, two-stage phase control method for high-power mode-programmable orbital angular momentum beam generation. Photonics Res. 2020;8(5):715. https://doi.org/10.1364/prj.388551.
    [81] Vincent P, et al. Stacked denoising autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. J Mach Learn Res. 2010;11:3371–408.
    [82] Vincent P, et al. Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, vol. 311. New York: ACM Press; 2008. p. 1096–103. https://doi.org/10.1145/1390156.1390294.
    [83] An Y, et al. Suppressing the Influence of CCD Vertical Blooming on M2 Determination through Deep Learning. In: 2019 18th International Conference on Optical Communications and Networks, ICOCN 2019(1); 2019. p. 2–4. https://doi.org/10.1109/ICOCN.2019.8934887.
    [84] Mathew RS, et al. The Raspberry Pi auto-aligner: Machine learning for automated alignment of laser beams. Rev Sci Instrum. 2021;92(1). https://doi.org/10.1063/5.0032588.
    [85] Arismar Cerqueira S. Recent progress and novel applications of photonic crystal fibers. Rep Prog Phys. 2010;73(2):024401. https://doi.org/10.1088/0034-4885/73/2/024401.
    [86] Chugh S, et al. Machine learning approach for computing optical properties of a photonic crystal fiber. Opt Express. 2019;27(25):36414. https://doi.org/10.1364/oe.27.036414.
    [87] Zibar D, et al. Inverse System Design Using Machine Learning: The Raman Amplifier Case. J Lightwave Technol. 2020;38(4):736–53. https://doi.org/10.1109/JLT.2019.2952179.
    [88] Zhou J, et al. Robust, compact, and flexible neural model for a fiber Raman amplifier. J Lightwave Technol. 2006;24(6):2362–7. https://doi.org/10.1109/JLT.2006.874602.
    [89] Singh S, Kaler RS. Performance optimization of EDFA-Raman hybrid optical amplifier using genetic algorithm. Opt Laser Technol. 2015;68:89–95. https://doi.org/10.1016/j.optlastec.2014.10.011.
    [90] M. Ionescu, A. Ghazisaeidi, and J. Renaudier, “Machine Learning Assisted Hybrid EDFA-Raman Amplifier Design for C+L Bands,” 2020 European Conference on Optical Communications, ECOC 2020(1), 2020–2022. 2020. https://doi.org/10.1109/ECOC48923.2020.9333241.
    [91] Jiang X, et al. Solving the nonlinear Schrödinger equation in optical fibers using physics-informed neural network. In: Optics InfoBase Conference Papers: OSA; 2021. p. 3–5. https://doi.org/10.1364/ofc.2021.m3h.8.
    [92] Teǧin U, et al. Controlling spatiotemporal nonlinearities in multimode fibers with deep neural networks. APL Photonics. 2020;5(3):030804. https://doi.org/10.1063/1.5138131.
    [93] Valensise CM, et al. Deep reinforcement learning control of white-light continuum generation. Optica. 2021;8(2):239. https://doi.org/10.1364/OPTICA.414634.
    [94] Su R, et al. Active coherent beam combining of a five-element, 800 W nanosecond fiber amplifier array. Opt Lett. 2012;37(19):3978. https://doi.org/10.1364/ol.37.003978.
    [95] Su R, et al. Active coherent beam combination of two high-power single-frequency nanosecond fiber amplifiers. Opt Lett. 2012;37(4):497. https://doi.org/10.1364/ol.37.000497.
    [96] Vu KT, et al. Adaptive pulse shape control in a diode-seeded nanosecond fiber MOPA system. Opt Express. 2006;14(23):10996. https://doi.org/10.1364/oe.14.010996.
    [97] Malinowski A, et al. High power pulsed fiber MOPA system incorporating electro-optic modulator based adaptive pulse shaping. Opt Express. 2009;17(23):20927. https://doi.org/10.1364/oe.17.020927.
    [98] Malinowski A, et al. High peak power, high-energy, high-average power pulsed fibre laser system with versatile pulse duration and shape. Optics InfoBase Conf Pap. 2013;38(22):4686. https://doi.org/10.1364/ol.38.004686.
    [99] Schimpf DN, et al. Compensation of pulse-distortion in saturated laser amplifiers. Opt Express. 2008;16(22):17637. https://doi.org/10.1364/oe.16.017637.
    [100] Shi H, et al. High-power diode-seeded thulium-doped fiber MOPA incorporating active pulse shaping. Appl Phys B Lasers Opt. 2016;122(10). https://doi.org/10.1007/s00340-016-6543-4.
    [101] Kutuzyan AA, et al. Dispersive regime of spectral compression. Quantum Electron. 2008;38(4):383–7. https://doi.org/10.1070/qe2008v038n04abeh013737.
    [102] Finot C, et al. Parabolic pulse generation and applications. In: 2nd IEEE LEOS Winter Topicals, WTM 2009 45(11); 2009. p. 110–1. https://doi.org/10.1109/LEOSWT.2009.4771681.
    [103] Boscolo S, Finot C. Artificial neural networks for nonlinear pulse shaping in optical fibers. Opt Laser Technol. 2020;131(February):106439. https://doi.org/10.1016/j.optlastec.2020.106439.
    [104] Boscolo S, Dudley JM, Finot C. Modelling self-similar parabolic pulses in optical fibres with a neural network. Results in Optics. 2021;3(November 2020):100066. https://doi.org/10.1016/j.rio.2021.100066.
    [105] Gupta RK, et al. Deep Learning Enabled Laser Speckle Wavemeter with a High Dynamic Range. Laser Photonics Rev. 2020;14(9):1–19. https://doi.org/10.1002/lpor.202000120.
    [106] Xiong W, et al. Deep learning of ultrafast pulses with a multimode fiber. APL Photonics. 2020;5(9). https://doi.org/10.1063/5.0007037.
    [107] Genty G, et al. Machine learning and applications in ultrafast photonics. Nat Photonics. 2021;15(2):91–101. https://doi.org/10.1038/s41566-020-00716-4.
    [108] Bendory T, Beinert R, Eldar YC. Fourier phase retrieval: Uniqueness and algorithms. Appl Numer Harmon Anal. 2017;(9783319698014):55–91. https://doi.org/10.1007/978-3-319-69802-1_2.
    [109] Escoto E, et al. Advanced phase retrieval for dispersion scan: a comparative study. J Opt Soc Am B. 2018;35(1):8. https://doi.org/10.1364/josab.35.000008.
    [110] Kane DJ. Principal components generalized projections: a review [Invited]. J Opt Soc Am B. 2008;25(6):A120. https://doi.org/10.1364/josab.25.00a120.
    [111] Sidorenko P, et al. Ptychographic reconstruction algorithm for FROG: Supreme robustness and super-resolution. In: 2016 Conference on Lasers and Electro-Optics, CLEO 2016 3(12); 2016. https://doi.org/10.1364/cleo_si.2016.stu4i.3.
    [112] Zahavy T, et al. Deep learning reconstruction of ultrashort pulses. Optica. 2018;5(5):666. https://doi.org/10.1364/OPTICA.5.000666.
    [113] Zhu Z, et al. Attosecond pulse retrieval from noisy streaking traces with conditional variational generative network. Sci Rep. 2020;10(1):1–7. https://doi.org/10.1038/s41598-020-62291-6.
    [114] White J, Chang Z. Attosecond streaking phase retrieval with neural network. Opt Express. 2019;27(4):4799. https://doi.org/10.1364/oe.27.004799.
    [115] Kokhanovskiy A, et al. Machine learning-based pulse characterization in figure-eight mode-locked lasers. Opt Lett. 2019;44(13):3410. https://doi.org/10.1364/ol.44.003410.
    [116] Bruning R, et al. Comparative analysis of numerical methods for the mode analysis of laser beams. Appl Opt. 2013;52(32):7769–77. https://doi.org/10.1364/AO.52.007769.
    [117] An Y, et al. Learning to decompose the modes in few-mode fibers with deep convolutional neural network. Opt Express. 2019;27(7):10127. https://doi.org/10.1364/oe.27.010127.
    [118] An Y, et al. Numerical mode decomposition for multimode fiber: From multi-variable optimization to deep learning. Opt Fiber Technol. 2019;52(June):101960. https://doi.org/10.1016/j.yofte.2019.101960.
    [119] An Y, et al. Deep Learning-Based Real-Time Mode Decomposition for Multimode Fibers. IEEE J Select Topics Quantum Electron. 2020;26(4):1–6. https://doi.org/10.1109/JSTQE.2020.2969511.
    [120] An Y, et al. Fast modal analysis for Hermite–Gaussian beams via deep learning. Appl Opt. 2020;59(7):1954. https://doi.org/10.1364/ao.377189.
    [121] Fan X, et al. Mitigating ambiguity by deep-learning-based modal decomposition method. Opt Commun. 2020;471(February):125845. https://doi.org/10.1016/j.optcom.2020.125845.
    [122] Rothe S, et al. Intensity-Only Mode Decomposition on Multimode Fibers Using a Densely Connected Convolutional Network. J Lightwave Technol. 2021;39(6):1672–9. https://doi.org/10.1109/JLT.2020.3041374.
    [123] Gao H, et al. Rapid Mode Decomposition of Few-Mode Fiber by Artificial Neural Network. J Lightwave Technol. 2021;39(19):6294–300. https://doi.org/10.1109/JLT.2021.3097501.
    [124] Scaggs M, Haas G. Real time laser beam analysis system for high power lasers. In: Laser Resonators and Beam Control XIII 7913; 2011. p. 791306. https://doi.org/10.1117/12.871369.
    [125] Du Y, Fu Y, Zheng L. Complex amplitude reconstruction for dynamic beam quality M^2 factor measurement with self-referencing interferometer wavefront sensor. Appl Opt. 2016;55(36):10180. https://doi.org/10.1364/ao.55.010180.
    [126] Han Z-G, et al. Determination of the laser beam quality factor (M^2) by stitching quadriwave lateral shearing interferograms with different exposures. Appl Opt. 2017;56(27):7596. https://doi.org/10.1364/ao.56.007596.
    [127] Pan S, et al. Real-time complex amplitude reconstruction method for beam quality M^2 factor measurement. Opt Express. 2017;25(17):20142. https://doi.org/10.1364/oe.25.020142.
    [128] Yoda H, Polynkin P, Mansuripur M. Beam quality factor of higher order modes in a step-index fiber. J Lightwave Technol. 2006;24(3):1350–5. https://doi.org/10.1109/JLT.2005.863337.
    [129] Huang L, et al. Real-time mode decomposition for few-mode fiber based on numerical method. Opt Express. 2015;23(4):4620. https://doi.org/10.1364/oe.23.004620.
    [130] Flamm D, et al. Fast M2 measurement for fiber beams based on modal analysis. Appl Opt. 2012;51(7):987–93. https://doi.org/10.1364/AO.51.000987.
    [131] An Y, et al. Deep learning enabled superfast and accurate M 2 evaluation for fiber beams. Opt Express. 2019;27(13):18683. https://doi.org/10.1364/OE.27.018683.
    [132] Pu G, et al. Automatic mode-locking fiber lasers: progress and perspectives. Sci China Inf Sci. 2020;63(6):1–24. https://doi.org/10.1007/s11432-020-2883-0.
    [133] Pu G, et al. Intelligent programmable mode-locked fiber laser with a human-like algorithm. Optica. 2019;6(3):362. https://doi.org/10.1364/optica.6.000362.
    [134] Brunton SL, Fu X, Kutz JN. Extremum-seeking control of a mode-locked laser. IEEE J Quantum Electron. 2013;49(10):852–61. https://doi.org/10.1109/JQE.2013.2280181.
    [135] Andral U, et al. Toward an autosetting mode-locked fiber laser cavity. J Opt Soc Am B. 2016;33(5):825. https://doi.org/10.1364/josab.33.000825.
    [136] Woodward RI, Kelleher EJR. Towards ‘smart lasers’: Self-optimisation of an ultrafast pulse source using a genetic algorithm. Sci Rep. 2016;6(November):1–9. https://doi.org/10.1038/srep37616.
    [137] Andra U, et al. Fiber laser mode locked through an evolutionary algorithm. In: Proceedings 2015 European Conference on Lasers and Electro-Optics - European Quantum Electronics Conference, CLEO/Europe-EQEC 2015 2(April); 2015. p. 2–6. https://doi.org/10.1364/optica.2.000275.
    [138] Fu X, Brunton SL, Nathan Kutz J. Classification of birefringence in mode-locked fiber lasers using machine learning and sparse representation. Opt Express. 2014;22(7):8585. https://doi.org/10.1364/oe.22.008585.
    [139] Brunton SL, Fu X, Kutz JN. Self-Tuning Fiber Lasers. IEEE J Select Topics Quantum Electron. 2014;20(5):464–71. https://doi.org/10.1109/JSTQE.2014.2336538.
    [140] Baumeister T, Brunton SL, Nathan Kutz J. Deep learning and model predictive control for self-tuning mode-locked lasers. J Opt Soc Am B. 2018;35(3):617. https://doi.org/10.1364/josab.35.000617.
    [141] Yan Q, et al. Low-latency deep-reinforcement learning algorithm for ultrafast fiber lasers. Photonics Res. 2021;9(8):1493. https://doi.org/10.1364/prj.428117.
    [142] Su R, et al. High Power Narrow-Linewidth Nanosecond Coherent Beam Combination. Ieee J Select Topics Quantum Electron. 2014;20(5):IEEE.
    [143] Chang H, et al. First experimental demonstration of coherent beam combining of more than 100 beams. Photonics Res. 2020;8(12):1943. https://doi.org/10.1364/prj.409788.
    [144] Goodno GD, et al. Active phase and polarization locking of a 14 kW fiber amplifier. Opt Lett. 2010;35(10):1542. https://doi.org/10.1364/ol.35.001542.
    [145] Goodno GD, et al. Brightness-scaling potential of actively phase-locked solid-state laser arrays. IEEE J Select Topics Quantum Electron. 2007;13(3):460–71. https://doi.org/10.1109/JSTQE.2007.896618.
    [146] Fsaifes I, et al. Coherent Beam combining of 37 femtosecond fiber amplifiers. In: Optics InfoBase Conference Papers Part F140-(14); 2019. p. 20152. https://doi.org/10.1364/oe.394031.
    [147] Kabeya D, et al. Efficient phase-locking of 37 fiber amplifiers by phase-intensity mapping in an optimization loop. Opt Express. 2017;25(12):13816. https://doi.org/10.1364/oe.25.013816.
    [148] Du Q, et al. Deterministic stabilization of eight-way 2D diffractive beam combining using pattern recognition. Opt Lett. 2019;44(18):4554. https://doi.org/10.1364/ol.44.004554.
    [149] Ahn HK, Kong HJ. Cascaded multi-dithering theory for coherent beam combining of multiplexed beam elements. Opt Express. 2015;23(9):12407. https://doi.org/10.1364/oe.23.012407.
    [150] Ahn HK, Kong HJ. Feasibility of cascaded multi-dithering technique for coherent addition of a large number of beam elements. Appl Opt. 2016;55(15):4101. https://doi.org/10.1364/ao.55.004101.
    [151] Ma Y, et al. Coherent beam combination with single frequency dithering technique. Opt Lett. 2010;35(9):1308. https://doi.org/10.1364/ol.35.001308.
    [152] Jiang M, et al. Coherent beam combining of fiber lasers using a CDMA-based single-frequency dithering technique. Appl Opt. 2017;56(15):4255. https://doi.org/10.1364/ao.56.004255.
    [153] Ma P, et al. 7.1 kW coherent beam combining system based on a seven-channel fiber amplifier array. Opt Laser Technol. 2021;140(October 2020):107016. https://doi.org/10.1016/j.optlastec.2021.107016.
    [154] Shpakovych M, et al. Experimental phase control of a 100 laser beam array with quasi-reinforcement learning of a neural network in an error reduction loop. Opt Express. 2021;29(8):12307. https://doi.org/10.1364/oe.419232.
    [155] Zhang X, et al. Coherent beam combination based on Q-learning algorithm. Opt Commun. 2021;490(February):126930. https://doi.org/10.1016/j.optcom.2021.126930.
    [156] Hou T, et al. High-power vortex beam generation enabled by a phased beam array fed at the nonfocal-plane. Opt Express. 2019;27(4):4046. https://doi.org/10.1364/oe.27.004046.
    [157] Hou T, et al. Deep-learning-based phase control method for tiled aperture coherent beam combining systems. High Power Laser Sci Eng. 2019;7:e59. https://doi.org/10.1017/hpl.2019.46.
    [158] Chen J, Wan C, Zhan Q. Engineering photonic angular momentum with structured light: a review. Adv Photonics. 2021;3(06):1–15. https://doi.org/10.1117/1.ap.3.6.064001.
    [159] Qiao Z, et al. Multi-vortex laser enabling spatial and temporal encoding. PhotoniX. 2020;1(1):13. https://doi.org/10.1186/s43074-020-00013-x.
    [160] Chen Y, Cai Y. Optical coherence structure: A novel tool for light manipulation. Sci China Technol Sci. 2021. https://doi.org/10.1007/s11431-021-1966-6.
    [161] Forbes A, de Oliveira M, Dennis MR. Structured light. Nat Photonics. 2021;15(4):253–62. https://doi.org/10.1038/s41566-021-00780-4.
    [162] Chang Q, et al. Phase-locking System in Fiber Laser Array through Deep Learning with Diffusers. In: 2020 Asia Communications and Photonics Conference, ACP 2020 and International Conference on Information Photonics and Optical Communications, IPOC 2020 - Proceedings; 2020. p. 7–9. https://doi.org/10.1364/acpc.2020.m4a.96.
    [163] Liu R, et al. Coherent beam combination far-field measuring method based on amplitude modulation and deep learning. Chin Opt Lett. 2020;18(4):041402. https://doi.org/10.3788/col202018.041402.
    [164] Wang D, et al. Stabilization of the 81-channel coherent beam combination using machine learning. Opt Express. 2021;29(4):5694. https://doi.org/10.1364/oe.414985.
    [165] Abadi M, et al. TensorFlow: A system for large-scale machine learning. In: Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016; 2016. p. 265–83.
    [166] Imambi S, Prakash KB, Kanagachidambaresan GR. PyTorch. 2021:87–104. https://doi.org/10.1007/978-3-030-57077-4_10.
    [167] Li K, Malik J. Learning to optimize. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings; 2017.
    [168] Andrychowicz M, et al. Learning to learn by gradient descent by gradient descent. In: Advances in Neural Information Processing Systems(Nips); 2016. p. 3988–96.
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  • 收稿日期:  2022-01-08
  • 录用日期:  2022-03-18
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