• Chinese Optics Letters
  • Vol. 23, Issue 3, 031401 (2025)
Haoyang Yu1,2, Siyu Lai1, Qiuying Ma3,*, Zhaohui Jiang1,2..., Dong Pan1,2 and Weihua Gui1,2|Show fewer author(s)
Author Affiliations
  • 1School of Automation, Central South University, Changsha 410083, China
  • 2State Key Laboratory of Precision Manufacturing for Extreme Service Performance, Central South University, Changsha 410083, China
  • 3Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
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    DOI: 10.3788/COL202523.031401 Cite this Article Set citation alerts
    Haoyang Yu, Siyu Lai, Qiuying Ma, Zhaohui Jiang, Dong Pan, Weihua Gui, "Dual feed-forward neural network for predicting complex nonlinear dynamics of mode-locked fiber laser under variable cavity parameters," Chin. Opt. Lett. 23, 031401 (2025) Copy Citation Text show less
    Neural network dataset generation process. WDM, wavelength division multiplexer; EDF, erbium-doped fiber; SMF, single-mode fiber; SA, saturable absorber; OC, 10%/90% coupler.
    Fig. 1. Neural network dataset generation process. WDM, wavelength division multiplexer; EDF, erbium-doped fiber; SMF, single-mode fiber; SA, saturable absorber; OC, 10%/90% coupler.
    Schematic diagram of the DFNN for mode-locked fiber laser dynamic prediction.
    Fig. 2. Schematic diagram of the DFNN for mode-locked fiber laser dynamic prediction.
    Training, validation, and test losses across epochs.
    Fig. 3. Training, validation, and test losses across epochs.
    Temporal evolution modeling of no soliton formation propagation dynamics under g0 = 1.145 m−1, LEDF = 0.263 m, and LSMF = 1.563 m. (a) The temporal evolution dynamics of the SSFM (top), the DFNN (middle), and the LSTM (bottom). (b) Temporal intensity at selected roundtrips predicted by the DFNN (dashed black lines), simulated with the SSFM (solid red lines).
    Fig. 4. Temporal evolution modeling of no soliton formation propagation dynamics under g0 = 1.145 m−1, LEDF = 0.263 m, and LSMF = 1.563 m. (a) The temporal evolution dynamics of the SSFM (top), the DFNN (middle), and the LSTM (bottom). (b) Temporal intensity at selected roundtrips predicted by the DFNN (dashed black lines), simulated with the SSFM (solid red lines).
    Temporal evolution modeling of single soliton formation propagation dynamics under g0 = 2.658 m−1, LEDF = 0.293 m, and LSMF = 1.552 m. (a) The temporal evolution dynamics of the SSFM (top), the DFNN (middle), and the LSTM (bottom). (b) Temporal intensity at selected roundtrips for detuned steady state predicted by the DFNN (dashed black lines), simulated with the SSFM (solid red lines).
    Fig. 5. Temporal evolution modeling of single soliton formation propagation dynamics under g0 = 2.658 m−1, LEDF = 0.293 m, and LSMF = 1.552 m. (a) The temporal evolution dynamics of the SSFM (top), the DFNN (middle), and the LSTM (bottom). (b) Temporal intensity at selected roundtrips for detuned steady state predicted by the DFNN (dashed black lines), simulated with the SSFM (solid red lines).
    Temporal evolution modeling of soliton molecule formation with narrow temporal separation propagation dynamics under g0 = 4.030 m−1, LEDF = 0.364 m, and LSMF = 1.178 m. (a) The temporal evolution dynamics of the SSFM (top), the DFNN (middle), and the LSTM (bottom). (b) Temporal intensity at selected roundtrips from detuned steady state to steady state predicted by the DFNN (dashed black lines), simulated with the SSFM (solid red lines).
    Fig. 6. Temporal evolution modeling of soliton molecule formation with narrow temporal separation propagation dynamics under g0 = 4.030 m−1, LEDF = 0.364 m, and LSMF = 1.178 m. (a) The temporal evolution dynamics of the SSFM (top), the DFNN (middle), and the LSTM (bottom). (b) Temporal intensity at selected roundtrips from detuned steady state to steady state predicted by the DFNN (dashed black lines), simulated with the SSFM (solid red lines).
    Temporal evolution modeling of soliton molecule formation with wide temporal separation propagation dynamics under g0 = 4.322 m−1, LEDF = 0.357 m, and LSMF = 1.406 m. (a) The temporal evolution dynamics of the SSFM (top), the DFNN (middle), and the LSTM (bottom). (b) Temporal intensity at selected roundtrips from detuned steady state to steady state predicted by the DFNN (dashed black lines), simulated with the SSFM (solid red lines).
    Fig. 7. Temporal evolution modeling of soliton molecule formation with wide temporal separation propagation dynamics under g0 = 4.322 m−1, LEDF = 0.357 m, and LSMF = 1.406 m. (a) The temporal evolution dynamics of the SSFM (top), the DFNN (middle), and the LSTM (bottom). (b) Temporal intensity at selected roundtrips from detuned steady state to steady state predicted by the DFNN (dashed black lines), simulated with the SSFM (solid red lines).
     DFNNLSTMSSFM
    NRMSE0.360.27N/A
    Training time (s)910859544N/A
    Simulation time (s)a0.441.9267
    Model memory (MB)17.3858.08N/A
    FLOPs (Mac)4.57 × 1062.07 × 108N/A
    Table 1. Performance Comparison Between the DFNN, the LSTM[27], and the SSFM
    Haoyang Yu, Siyu Lai, Qiuying Ma, Zhaohui Jiang, Dong Pan, Weihua Gui, "Dual feed-forward neural network for predicting complex nonlinear dynamics of mode-locked fiber laser under variable cavity parameters," Chin. Opt. Lett. 23, 031401 (2025)
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