[1] D. R. Solli, B. Jalali. Analog optical computing. Nat. Photonics, 9, 704(2015).

[2] P. Grelu, N. Akhmediev. Dissipative solitons for mode-locked lasers. Nat. Photonics, 6, 84(2012).

[3] R. Weill, A. Bekker, V. Smulakovsky, B. Fischer, O. Gat. Noise-mediated Casimir-like pulse interaction mechanism in lasers. Optica, 3, 189(2016).

[4] E. Ding, S. Lefrancois, J. N. Kutz, F. W. Wise. Scaling fiber lasers to large mode area: an investigation of passive mode-locking using a multi-mode fiber. IEEE J. Quantum Electron., 47, 597(2011).

[5] K. J. Zhao, C. X. Gao, X. S. Xiao, C. X. Yang. Real-time collision dynamics of vector solitons in a fiber laser. Photonics Res., 9, 289(2021).

[6] J. He, P. Wang, R. He, C. Liu, M. Zhou, Y. Liu, Y. Yue, B. Liu, D. Xing, K. Zhu, K. Chang, Z. Wang. Elastic and inelastic collision dynamics between soliton molecules and a single soliton. Opt. Express, 30, 14218(2022).

[7] J. S. Peng, H. P. Zeng. Build-up of dissipative optical soliton molecules via diverse soliton interactions. Laser Photonics Rev., 12, 1800009(2018).

[8] X. M. Liu, X. K. Yao, Y. D. Cui. Real-time observation of the buildup of soliton molecules. Phys. Rev. Lett., 121, 023905(2018).

[9] Y. Q. Du, C. Zeng, Z. W. He, Q. Gao, D. Mao. Coherent dissipative soliton intermittency in ultrafast fiber lasers. Chin. Opt. Lett., 20, 011401(2022).

[10] M. Suzuki, O. Boyraz, H. Asghari, P. Trinh, H. Kuroda, B. Jalali. Spectral periodicity in soliton explosions on a broadband mode-locked Yb fiber laser using time-stretch spectroscopy. Opt. Lett., 43, 1862(2018).

[11] L. Salmela, N. Tsipinakis, A. Foi, C. Billet, J. M. Dudley, G. Genty. Predicting ultrafast nonlinear dynamics in fibre optics with a recurrent neural network. Nat. Mach. Intell., 3, 344(2021).

[12] M. Narhi, L. Salmela, J. Toivonen, C. Billet, J. M. Dudley, G. Genty. Machine learning analysis of extreme events in optical fibre modulation instability. Nat. Commun., 9, 4923(2018).

[13] T. Zahavy, A. Dikopoltsev, D. Moss, G. I. Haham, O. Cohen, S. Mannor, M. Segev. Deep learning reconstruction of ultrashort pulses. Optica, 5, 666(2018).

[14] J. Y. He, C. Y. Li, P. Wang, C. C. Liu, Y. G. Liu, B. Liu, D. K. Xing, Z. Wang. soliton molecule dynamics evolution prediction based on LSTM neural networks. IEEE Photon. Technol. Lett., 34, 193(2022).

[15] P. Y. Lu, S. Kim, M. Soljacic. Extracting interpretable physical parameters from spatiotemporal systems using unsupervised learning. Phys. Rev. X, 10, 031056(2020).

[16] R. Guidotti, A. Monreale, S. Ruggieri, F. Turin, F. Giannotti, D. Pedreschi. A survey of methods for explaining black box models. ACM Comput. Surv., 51, 1(2019).

[17] G. Genty, L. Salmela, J. M. Dudley, D. Brunner, A. Kokhanovskiy, S. Kobtsev, S. K. Turitsyn. Machine learning and applications in ultrafast photonics. Nat. Photonics, 15, 91(2021).

[18] X. Wei, J. C. Jing, Y. Shen, L. V. Wang. Harnessing a multi-dimensional fibre laser using genetic wavefront shaping. Light Sci. Appl., 9, 149(2020).

[19] G. Pu, L. Yi, L. Zhang, C. Luo, Z. Li, W. Hu. Intelligent control of mode-locked femtosecond pulses by time-stretch-assisted real-time spectral analysis. Light Sci. Appl., 9, 13(2020).

[20] A. J. Linot, M. D. Graham. Deep learning to discover and predict dynamics on an inertial manifold. Phys. Rev. E, 101, 062209(2020).

[21] C. Y. Li, J. Y. He, R. J. He, Y. E. Liu, Y. Yue, W. W. Liu, L. H. Zhang, L. F. Zhu, M. J. Zhou, K. Y. Zhu, Z. Wang. Analysis of real-time spectral interference using a deep neural network to reconstruct multi-soliton dynamics in mode-locked lasers. APL Photonics, 5, 116101(2020).

[22] G. E. Hinton, R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313, 504(2006).

[23] R. Iten, T. Metger, H. Wilming, L. del Rio. Discovering physical concepts with neural networks. Phys. Rev. Lett., 124, 010508(2020).

[24] X. Ding, H. Chate, P. Cvitanovic, E. Siminos, K. A. Takeuchi. Estimating the dimension of an inertial manifold from unstable periodic orbits. Phys. Rev. Lett., 117, 024101(2016).

[25] M. Raissi, G. E. Karniadakis. Hidden physics models: machine learning of nonlinear partial differential equations. J. Comput. Phys., 357, 125(2018).

[26] B. Lusch, J. N. Kutz, S. L. Brunton. Deep learning for universal linear embeddings of nonlinear dynamics. Nat. Commun., 9, 4950(2018).

[27] P. R. Vlachas, G. Arampatzis, C. Uhler, P. Koumoutsakos. Multiscale simulations of complex systems by learning their effective dynamics. Nat. Mach. Intell., 4, 359(2022).

[28] S. H. Rudy, S. L. Brunton, J. L. Proctor, J. N. Kutz. Data-driven discovery of partial differential equations. Sci. Adv., 3, e1602614(2017).

[29] R. He, J. He, C. Li, L. Zhang, L. Zhu, M. Zhou, K. Zhu, Y. Liu, Y. Yue, Z. Wang. Transition between optical turbulence and dissipative solitons in a complex Ginzburg-Landau equation with quasi-CW noise. Phys. Rev. A, 103, 043515(2021).

[30] M. J. Ablowitz, T. P. Horikis, S. D. Nixon. Soliton strings and interactions in mode-locked lasers. Opt. Commun., 282, 4127(2009).

[31] R. He, Z. Wang, Y. Liu, Z. Wang, H. Liang, S. Han, J. He. Dynamic evolution of pulsating solitons in a dissipative system with the gain saturation effect. Opt. Express, 26, 33116(2018).