IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v152y2021ics0960077921007475.html
   My bibliography  Save this article

Predicting the dynamic process and model parameters of the vector optical solitons in birefringent fibers via the modified PINN

Author

Listed:
  • Wu, Gang-Zhou
  • Fang, Yin
  • Wang, Yue-Yue
  • Wu, Guo-Cheng
  • Dai, Chao-Qing

Abstract

A modified physics-informed neural network is used to predict the dynamics of optical pulses including one-soliton, two-soliton, and rogue wave based on the coupled nonlinear Schrödinger equation in birefringent fibers. At the same time, the elastic collision process of the mixed bright-dark soliton is predicted. Compared the predicted results with the exact solution, the modified physics-informed neural network method is proven to be effective to solve the coupled nonlinear Schrödinger equation. Moreover, the dispersion coefficients and nonlinearity coefficients of the coupled nonlinear Schrödinger equation can be learned by modified physics-informed neural network. This provides a reference for us to use deep learning methods to study the dynamic characteristics of solitons in optical fibers.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:152:y:2021:i:c:s0960077921007475
    DOI: 10.1016/j.chaos.2021.111393
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077921007475
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2021.111393?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. Nandy, Sudipta & Barthakur, Abhijit, 2021. "Dark-bright soliton interactions in coupled nonautonomous nonlinear Schrödinger equation with complex potentials," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    2. Mikko Närhi & Lauri Salmela & Juha Toivonen & Cyril Billet & John M. Dudley & Goëry Genty, 2018. "Machine learning analysis of extreme events in optical fibre modulation instability," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
    3. Du, Zhong & Tian, Bo & Qu, Qi-Xing & Chai, Han-Peng & Zhao, Xue-Hui, 2020. "Vector breathers for the coupled fourth-order nonlinear Schrödinger system in a birefringent optical fiber," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
    4. Surazhevsky, I.A. & Demin, V.A. & Ilyasov, A.I. & Emelyanov, A.V. & Nikiruy, K.E. & Rylkov, V.V. & Shchanikov, S.A. & Bordanov, I.A. & Gerasimova, S.A. & Guseinov, D.V. & Malekhonova, N.V. & Pavlov, D, 2021. "Noise-assisted persistence and recovery of memory state in a memristive spiking neuromorphic network," Chaos, Solitons & Fractals, Elsevier, vol. 146(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. 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).
    2. 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).
    3. Cai, Yue-Jin & Wu, Jian-Wen & Lin, Ji, 2022. "Nondegenerate N-soliton solutions for Manakov system," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    4. 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).
    5. 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).
    6. 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).
    7. 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).
    8. Fang, Yin & Zhu, Bo-Wei & Bo, Wen-Bo & Wang, Yue-Yue & Dai, Chao-Qing, 2023. "Data-driven prediction of spatial optical solitons in fractional diffraction," Chaos, Solitons & Fractals, Elsevier, vol. 175(P2).
    9. 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).

    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. 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).
    2. Kim, Tae-Hyeon & Kim, Sungjoon & Hong, Kyungho & Park, Jinwoo & Hwang, Yeongjin & Park, Byung-Gook & Kim, Hyungjin, 2021. "Multilevel switching memristor by compliance current adjustment for off-chip training of neuromorphic system," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    3. Choi, Woo Sik & Jang, Jun Tae & Kim, Donguk & Yang, Tae Jun & Kim, Changwook & Kim, Hyungjin & Kim, Dae Hwan, 2022. "Influence of Al2O3 layer on InGaZnO memristor crossbar array for neuromorphic applications," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    4. Cui, Kaiyan & Song, Zhanjie & Zhang, Shuo, 2022. "Stability of neutral-type neural network with Lévy noise and mixed time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    5. Jin, Yanfei & Wang, Haotian & Xu, Pengfei, 2023. "Noise-induced enhancement of stability and resonance in a tri-stable system with time-delayed feedback," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    6. Ai, Hao & Yang, GuiJiang & Liu, Wei & Wang, Qiubao, 2023. "A fast search method for optimal parameters of stochastic resonance based on stochastic bifurcation and its application in fault diagnosis of rolling bearings," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    7. Li, Jun-Jie & Zhang, Hui-Cong, 2023. "Propagation dynamics of hybrid-order Poincaré beams in thermal nonlocal media," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    8. Lin, Lifeng & Lin, Tianzhen & Zhang, Ruoqi & Wang, Huiqi, 2023. "Generalized stochastic resonance in a time-delay fractional oscillator with damping fluctuation and signal-modulated noise," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    9. Maldonado, D. & Aguilera-Pedregosa, C. & Vinuesa, G. & García, H. & Dueñas, S. & Castán, H. & Aldana, S. & González, M.B. & Moreno, E. & Jiménez-Molinos, F. & Campabadal, F. & Roldán, J.B., 2022. "An experimental and simulation study of the role of thermal effects on variability in TiN/Ti/HfO2/W resistive switching nonlinear devices," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    10. Tan, Yiping & Cai, Yongli & Sun, Xiaodan & Wang, Kai & Yao, Ruoxia & Wang, Weiming & Peng, Zhihang, 2022. "A stochastic SICA model for HIV/AIDS transmission," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    11. Han, Cheng & Wang, Yan & Jiang, Daqing, 2023. "Dynamics analysis of a stochastic HIV model with non-cytolytic cure and Ornstein–Uhlenbeck process," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    12. Hu, Xiang & Yin, Zhixiang, 2022. "A study of the pulse propagation with a generalized Kudryashov equation," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    13. Ladeynov, D.A. & Egorov, D.G. & Pankratov, A.L., 2023. "Stochastic versus dynamic resonant activation to enhance threshold detector sensitivity," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    14. Zhang, Dongjian & Ma, Qihua & Dong, Hailiang & Liao, He & Liu, Xiangyu & Zha, Yibin & Zhang, Xiaoxiao & Qian, Xiaomin & Liu, Jin & Gan, Xuehui, 2023. "Time-delayed feedback bistable stochastic resonance system and its application in the estimation of the Polyester Filament Yarn tension in the spinning process," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    15. Vasileiadis, Nikolaos & Loukas, Panagiotis & Karakolis, Panagiotis & Ioannou-Sougleridis, Vassilios & Normand, Pascal & Ntinas, Vasileios & Fyrigos, Iosif-Angelos & Karafyllidis, Ioannis & Sirakoulis,, 2021. "Multi-level resistance switching and random telegraph noise analysis of nitride based memristors," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    16. Kim, Dahye & Kim, Sunghun & Kim, Sungjun, 2021. "Logic-in-memory application of CMOS compatible silicon nitride memristor," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    17. Wang, Yang & Li, Huanyun & Guan, Yan & Chen, Mingshu, 2022. "Predefined-time chaos synchronization of memristor chaotic systems by using simplified control inputs," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    18. Chen, Ruyin & Xiong, Yue & Zhuge, Shengying & Li, Zekun & Chen, Qitie & He, Zhifen & Wu, Dingqiang & Hou, Fang & Zhou, Jiawei, 2023. "Regulation and prediction of multistable perception alternation," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    19. Huang, Lilian & Liu, Jin & Xiang, Jianhong & Zhang, Zefeng & Du, Xiuli, 2022. "A construction method of N-dimensional non-degenerate discrete memristive hyperchaotic map," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    20. Ping, Zhu, 2023. "Analytical equivalent transformation method for nonlinear stochastic dynamics with multiple noises in high dimensions," Chaos, Solitons & Fractals, Elsevier, vol. 176(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:152:y:2021:i:c:s0960077921007475. 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.