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Dynamics of diverse data-driven solitons for the three-component coupled nonlinear Schrödinger model by the MPS-PINN method

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Abstract

We improve the physical information neural network by adding multiple parallel subnets to predict seven types of soliton dynamics, such as one soliton, two solitons and soliton molecules, and rogue waves for the three-component coupled nonlinear Schrödinger model. In particular, we use this improved method to predict the collision process between two-bright–one-dark solitons and vector soliton molecules. Through the detailed demonstration of the predicted results from different ways, we verify the validity of this improved method for solving the three-component coupled nonlinear Schrödinger model. This supplies us a reference to research the complicated dynamics of solitons by the machine learning method.

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The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work is funded by Zhejiang Provincial Natural Science Foundation of China (Grant No.LR20A050001), the National Natural Science Foundation of China (Grant No. 12075210), Scientific Research and Developed Fund of Zhejiang A&F University (Grant No. 2021FR0009) and the National Training Programs of Innovation and Entrepreneurship for Undergraduates of China (Grant No. 202210341025).

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Author notes

  1. Xue-Kun Wen and Gang-Zhou Wu are contributed equally to this work.

Authors and Affiliations

  1. College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Lin’an, Zhejiang, 311300, People’s Republic of China

    Xue-Kun Wen, Gang-Zhou Wu, Wei Liu & Chao-Qing Dai

Authors
  1. Xue-Kun Wen
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  2. Gang-Zhou Wu
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  3. Wei Liu
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  4. Chao-Qing Dai
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Correspondence to Chao-Qing Dai.

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Wen, XK., Wu, GZ., Liu, W. et al. Dynamics of diverse data-driven solitons for the three-component coupled nonlinear Schrödinger model by the MPS-PINN method. Nonlinear Dyn 109, 3041–3050 (2022). https://doi.org/10.1007/s11071-022-07583-4

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  • DOI: https://doi.org/10.1007/s11071-022-07583-4

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