PhotoniX, Volume. 3, Issue 1, 16(2022)
Fiber laser development enabled by machine learning: review and prospect
[13] [13] Shang C, et al. Review on wavelength-tunable pulsed fiber lasers based on 2D materials. Opt Laser Technol. 2020;131(September 2019). .
[14] [14] Dragic PD, Cavillon M, Ballato J. Materials for optical fiber lasers: A review. Appl Phys Rev. 2018;5(4). .
[22] [22] Zuo C, et al. Deep learning in optical metrology: a review. Light Sci Appl. 2022;11(1). .
[28] [28] Malkiel I, et al. Plasmonic nanostructure design and characterization via Deep Learning. Light Sci Appl. 2018;7(1). .
[33] [33] Mitchell TM. Machine Learning. New York: McGraw-Hill; 1997.
[34] [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] [35] Tamir JI, Yu SX, Lustig M. Unsupervised Deep Basis Pursuit: Learning inverse problems without ground-truth data; 2019. p. 1–5.
[37] [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.
[39] [39] Qiu J, et al. A survey of machine learning for big data processing. Eurasip J Adv Signal Process. 2016;(1). .
[48] [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] [49] Solomatine D, See LM, Abrahart RJ. Data-Driven Modelling: Concepts, Approaches and Experiences. Pract Hydroinf. 2008:17–30. .
[52] [52] Raissi M. Deep hidden physics models: Deep learning of nonlinear partial differential equations. J Mach Learn Res. 2018;19:1–24.
[58] [58] Salehinejad H, et al. Recent Advances in Recurrent Neural Networks; 2017. p. 1–21.
[60] [60] Zhang J, et al. Why gradient clipping accelerates training: A theoretical justification for adaptivity; 2019. p. 1–21.
[61] [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] [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.
[64] [64] F. P. Such et al., “Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning” (2017).
[65] [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.
[69] [69] Mnih V, et al. Playing Atari with Deep Reinforcement Learning. In: Deep Reinforcement Learning; 2013. p. 135–60.
[72] [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. .
[78] [78] Tünnermann H, Shirakawa A. Deep reinforcement learning for tiled aperture beam combining in a simulated environment. JPhys Photonics. 2021;3(1). .
[79] [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). .
[81] [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.
[84] [84] Mathew RS, et al. The Raspberry Pi auto-aligner: Machine learning for automated alignment of laser beams. Rev Sci Instrum. 2021;92(1). .
[90] [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. .
[100] [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). .
[106] [106] Xiong W, et al. Deep learning of ultrafast pulses with a multimode fiber. APL Photonics. 2020;5(9). .
[108] [108] Bendory T, Beinert R, Eldar YC. Fourier phase retrieval: Uniqueness and algorithms. Appl Numer Harmon Anal. 2017;(9783319698014):55–91. .
[142] [142] Su R, et al. High Power Narrow-Linewidth Nanosecond Coherent Beam Combination. Ieee J Select Topics Quantum Electron. 2014;20(5):IEEE.
[160] [160] Chen Y, Cai Y. Optical coherence structure: A novel tool for light manipulation. Sci China Technol Sci. 2021. .
[165] [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] [166] Imambi S, Prakash KB, Kanagachidambaresan GR. PyTorch. 2021:87–104. .
[167] [167] Li K, Malik J. Learning to optimize. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings; 2017.
[168] [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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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, 2022, 3(1): 16
Category: Research Articles
Received: Jan. 8, 2022
Accepted: Mar. 18, 2022
Published Online: Jul. 10, 2023
The Author Email: Su Rongtao (surongtao@126.com), Zhou Pu (zhoupu203@163.com)