IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v162y2022ics0960077922006518.html
   My bibliography  Save this article

Predicting the dynamic process and model parameters of vector optical solitons under coupled higher-order effects via WL-tsPINN

Author

Listed:
  • Zhu, Bo-Wei
  • Fang, Yin
  • Liu, Wei
  • Dai, Chao-Qing

Abstract

We propose the two-subnet physical information neural network with the weighted loss function (WL-tsPINN) to study the higher-order effects of ultra-short pulses in birefringence fiber transmission and analyze the formation mechanism of vector solitons. We predict the dynamical process of mixed-type single/double soliton and soliton molecules based on the higher-order coupled nonlinear Schrödinger equation (CNLSE) by this WL-tsPINN method. Moreover, we deduce the physical coefficients of the higher-order CNLSE from the mixed single soliton solution. Deep learning based on neural network is a powerful tool for further study of higher-order CNLSE and has potential significance for further study of soliton dynamics.

Suggested Citation

  • Zhu, Bo-Wei & Fang, Yin & Liu, Wei & Dai, Chao-Qing, 2022. "Predicting the dynamic process and model parameters of vector optical solitons under coupled higher-order effects via WL-tsPINN," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    • Handle: RePEc:eee:chsofr:v:162:y:2022:i:c:s0960077922006518
    DOI: 10.1016/j.chaos.2022.112441
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077922006518
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2022.112441?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Pan & Ma, Tian-Ping & Qi, Feng-Hua, 2021. "Analytical solutions for the coupled Hirota equations in the firebringent fiber," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    2. Fang, Yin & Wu, Gang-Zhou & Kudryashov, Nikolay A. & Wang, Yue-Yue & Dai, Chao-Qing, 2022. "Data-driven soliton solutions and model parameters of nonlinear wave models via the conservation-law constrained neural network method," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    3. Rajan, M.S. Mani & Veni, S. Saravana, 2022. "Impact of external potential and non-isospectral functions on optical solitons and modulation instability in a cubic quintic nonlinear media," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    4. Wu, Gang-Zhou & Fang, Yin & Wang, Yue-Yue & Wu, Guo-Cheng & Dai, Chao-Qing, 2021. "Predicting the dynamic process and model parameters of the vector optical solitons in birefringent fibers via the modified PINN," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    5. Guo, Bo-Ling & Wang, Yu-Feng, 2016. "Mixed-type soliton solutions for the N-coupled higher-order nonlinear schrödinger equation in optical fibers," Chaos, Solitons & Fractals, Elsevier, vol. 93(C), pages 246-251.
    6. Arnous, Ahmed H. & Biswas, Anjan & Yıldırım, Yakup & Zhou, Qin & Liu, Wenjun & Alshomrani, Ali S. & Alshehri, Hashim M., 2022. "Cubic–quartic optical soliton perturbation with complex Ginzburg–Landau equation by the enhanced Kudryashov’s method," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    7. Triki, Houria & Zhou, Qin & Liu, Wenjun & Biswas, Anjan & Moraru, Luminita & Yıldırım, Yakup & Alshehri, Hashim M. & Belic, Milivoj R., 2022. "Chirped optical soliton propagation in birefringent fibers modeled by coupled Fokas-Lenells system," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Elsayed M. E. Zayed & Mohamed E. M. Alngar & Reham M. A. Shohib, 2022. "Dispersive Optical Solitons to Stochastic Resonant NLSE with Both Spatio-Temporal and Inter-Modal Dispersions Having Multiplicative White Noise," Mathematics, MDPI, vol. 10(17), pages 1-18, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wu, Gang-Zhou & Fang, Yin & Kudryashov, Nikolay A. & Wang, Yue-Yue & Dai, Chao-Qing, 2022. "Prediction of optical solitons using an improved physics-informed neural network method with the conservation law constraint," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    2. Fang, Yin & Bo, Wen-Bo & Wang, Ru-Ru & Wang, Yue-Yue & Dai, Chao-Qing, 2022. "Predicting nonlinear dynamics of optical solitons in optical fiber via the SCPINN," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    3. Han, Tianyong & Li, Zhao & Li, Chenyu, 2023. "Bifurcation analysis, stationary optical solitons and exact solutions for generalized nonlinear Schrödinger equation with nonlinear chromatic dispersion and quintuple power-law of refractive index in ," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    4. Nikolay A. Kudryashov, 2023. "Hamiltonians of the Generalized Nonlinear Schrödinger Equations," Mathematics, MDPI, vol. 11(10), pages 1-12, May.
    5. Li, Zhao & Huang, Chun, 2023. "Bifurcation, phase portrait, chaotic pattern and optical soliton solutions of the conformable Fokas–Lenells model in optical fibers," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    6. Nikolay A. Kudryashov, 2022. "Optical Solitons of the Generalized Nonlinear Schrödinger Equation with Kerr Nonlinearity and Dispersion of Unrestricted Order," Mathematics, MDPI, vol. 10(18), pages 1-9, September.
    7. Pu, Jun-Cai & Chen, Yong, 2022. "Data-driven vector localized waves and parameters discovery for Manakov system using deep learning approach," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    8. Zhang, Yabin & Wang, Lei & Zhang, Peng & Luo, Haotian & Shi, Wanlin & Wang, Xin, 2022. "The nonlinear wave solutions and parameters discovery of the Lakshmanan-Porsezian-Daniel based on deep learning," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    9. Cao, Qi-Hao & Geng, Kai-Li & Zhu, Bo-Wei & Wang, Yue-Yue & Li, Ji-tao & Dai, Chao-Qing, 2023. "Annular rogue waves in whispering gallery mode optical resonators," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    10. Nikolay A. Kudryashov & Sofia F. Lavrova, 2023. "Painlevé Test, Phase Plane Analysis and Analytical Solutions of the Chavy–Waddy–Kolokolnikov Model for the Description of Bacterial Colonies," Mathematics, MDPI, vol. 11(14), pages 1-17, July.
    11. Ahmed H. Arnous & Luminita Moraru, 2022. "Optical Solitons with the Complex Ginzburg–Landau Equation with Kudryashov’s Law of Refractive Index," Mathematics, MDPI, vol. 10(19), pages 1-13, September.
    12. Jaganathan, Meiyazhagan & Bakthavatchalam, Tamil Arasan & Vadivel, Murugesan & Murugan, Selvakumar & Balu, Gopinath & Sankarasubbu, Malaikannan & Ramaswamy, Radha & Sethuraman, Vijayalakshmi & Malomed, 2023. "Data-driven multi-valley dark solitons of multi-component Manakov Model using Physics-Informed Neural Networks," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    13. Chen, Junchao & Song, Jin & Zhou, Zijian & Yan, Zhenya, 2023. "Data-driven localized waves and parameter discovery in the massive Thirring model via extended physics-informed neural networks with interface zones," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    14. Zhou, Qin & Triki, Houria & Xu, Jiakun & Zeng, Zhongliang & Liu, Wenjun & Biswas, Anjan, 2022. "Perturbation of chirped localized waves in a dual-power law nonlinear medium," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    15. Yu, Weitian & Luan, Zitong & Zhang, Hongxin & Liu, Wenjun, 2022. "Collisions of three higher order dark double- and single-hump solitons in optical fiber," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    16. Zhong, WenYe & Qin, Pei & Zhong, Wei-Ping & Belić, Milivoj, 2022. "Two-dimensional rogue wave clusters in self-focusing Kerr-media," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    17. Fang, Yin & Wu, Gang-Zhou & Kudryashov, Nikolay A. & Wang, Yue-Yue & Dai, Chao-Qing, 2022. "Data-driven soliton solutions and model parameters of nonlinear wave models via the conservation-law constrained neural network method," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    18. Cai, Yue-Jin & Wu, Jian-Wen & Lin, Ji, 2022. "Nondegenerate N-soliton solutions for Manakov system," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:162:y:2022:i:c:s0960077922006518. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.