From data to dynamics: Reconstructing soliton collision phenomena in optical fibers using a convolutional autoencoder
In this study, a convolutional autoencoder is constructed to extract and reconstruct the dynamical processes of soliton collisions in optical fibers. The model demonstrates exceptional reconstruction capabilities, accurately capturing the evolution of optical event horizons and reproducing nonlinear...
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| Vydané v: | Results in physics Ročník 67; s. 108027 |
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Elsevier B.V
01.12.2024
Elsevier |
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| Abstract | In this study, a convolutional autoencoder is constructed to extract and reconstruct the dynamical processes of soliton collisions in optical fibers. The model demonstrates exceptional reconstruction capabilities, accurately capturing the evolution of optical event horizons and reproducing nonlinear phenomena such as complex frequency conversions and energy exchange processes. The reconstruction results show high consistency with the numerical simulations, with RMSE values of 0.0220 and 0.0174 in the temporal and frequency domains, respectively. Additionally, by adjusting the training parameters of the convolutional autoencoder model, its reconstruction performance for nonlinear dynamic processes was further validated. This method is expected to provide a different perspective for studying nonlinear phenomena in optical fibers while reducing the consumption of computational resources.
•Developed a convolutional autoencoder to capture soliton-probe interactions.•Achieved high visual consistency and low RMSE in temporal and frequency domains.•Validated robustness and accuracy across different training parameters. |
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| AbstractList | In this study, a convolutional autoencoder is constructed to extract and reconstruct the dynamical processes of soliton collisions in optical fibers. The model demonstrates exceptional reconstruction capabilities, accurately capturing the evolution of optical event horizons and reproducing nonlinear phenomena such as complex frequency conversions and energy exchange processes. The reconstruction results show high consistency with the numerical simulations, with RMSE values of 0.0220 and 0.0174 in the temporal and frequency domains, respectively. Additionally, by adjusting the training parameters of the convolutional autoencoder model, its reconstruction performance for nonlinear dynamic processes was further validated. This method is expected to provide a different perspective for studying nonlinear phenomena in optical fibers while reducing the consumption of computational resources. In this study, a convolutional autoencoder is constructed to extract and reconstruct the dynamical processes of soliton collisions in optical fibers. The model demonstrates exceptional reconstruction capabilities, accurately capturing the evolution of optical event horizons and reproducing nonlinear phenomena such as complex frequency conversions and energy exchange processes. The reconstruction results show high consistency with the numerical simulations, with RMSE values of 0.0220 and 0.0174 in the temporal and frequency domains, respectively. Additionally, by adjusting the training parameters of the convolutional autoencoder model, its reconstruction performance for nonlinear dynamic processes was further validated. This method is expected to provide a different perspective for studying nonlinear phenomena in optical fibers while reducing the consumption of computational resources. •Developed a convolutional autoencoder to capture soliton-probe interactions.•Achieved high visual consistency and low RMSE in temporal and frequency domains.•Validated robustness and accuracy across different training parameters. |
| ArticleNumber | 108027 |
| Author | Huang, Longnv Xu, Qibo Zeng, Qilin Rong, Jifang Yuan, Xiaofang Yang, Hua |
| Author_xml | – sequence: 1 givenname: Qibo surname: Xu fullname: Xu, Qibo organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China – sequence: 2 givenname: Jifang surname: Rong fullname: Rong, Jifang organization: Department of Information Engineering, Hunan Mechanical and Electrical of Polytechnic, Changsha, 410151, Hunan, China – sequence: 3 givenname: Qilin surname: Zeng fullname: Zeng, Qilin organization: Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, 100094, China – sequence: 4 givenname: Xiaofang surname: Yuan fullname: Yuan, Xiaofang organization: Department of Electric Engineering and Information, Hunan University, Changsha, 410082, Hunan, China – sequence: 5 givenname: Longnv surname: Huang fullname: Huang, Longnv organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China – sequence: 6 givenname: Hua orcidid: 0000-0002-9658-7410 surname: Yang fullname: Yang, Hua email: huayang@hnu.edu.cn organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China |
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| Cites_doi | 10.1016/j.physrep.2012.09.004 10.1126/science.1153625 10.1007/s11071-020-05865-3 10.1103/PhysRevLett.106.163901 10.1103/PhysRevLett.122.010404 10.1364/OE.447201 10.1109/TPAMI.2013.50 10.1038/s42256-021-00297-z 10.1016/j.neunet.2014.09.003 10.1093/nsr/nwaa111 10.1007/s11071-024-09590-z 10.1016/j.chaos.2024.114995 10.1364/OL.41.001372 10.1364/OE.394896 10.1016/j.neucom.2015.08.104 10.1007/s11071-021-06283-9 10.1063/1.5091811 10.1063/5.0210903 10.1088/1361-6382/ac7506 10.1109/ACCESS.2019.2905511 10.1364/OL.472267 10.1038/nphoton.2008.285 10.1364/OL.448571 10.1364/OE.503978 10.3788/COL202220.111901 10.1038/ncomms5969 10.1364/JOSAB.482200 10.1364/OE.507167 10.1103/PhysRevLett.95.213902 10.1109/JLT.2020.3003447 10.1038/s42005-020-00414-1 10.1016/j.yofte.2021.102540 |
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| Keywords | Soliton collisions Convolutional autoencoder Optical event horizon |
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| SubjectTerms | Convolutional autoencoder Optical event horizon Soliton collisions |
| Title | From data to dynamics: Reconstructing soliton collision phenomena in optical fibers using a convolutional autoencoder |
| URI | https://dx.doi.org/10.1016/j.rinp.2024.108027 https://doaj.org/article/e93ebfcbf5444bbfa987061bacc51be0 |
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