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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Results in Physics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211379724007125 |
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| _version_ | 1850250586856882176 |
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| author | Qibo Xu Jifang Rong Qilin Zeng Xiaofang Yuan Longnv Huang Hua Yang |
| author_facet | Qibo Xu Jifang Rong Qilin Zeng Xiaofang Yuan Longnv Huang Hua Yang |
| author_sort | Qibo Xu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e93ebfcbf5444bbfa987061bacc51be0 |
| institution | OA Journals |
| issn | 2211-3797 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Physics |
| spelling | doaj-art-e93ebfcbf5444bbfa987061bacc51be02025-08-20T01:58:08ZengElsevierResults in Physics2211-37972024-12-016710802710.1016/j.rinp.2024.108027From data to dynamics: Reconstructing soliton collision phenomena in optical fibers using a convolutional autoencoderQibo Xu0Jifang Rong1Qilin Zeng2Xiaofang Yuan3Longnv Huang4Hua Yang5College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, ChinaDepartment of Information Engineering, Hunan Mechanical and Electrical of Polytechnic, Changsha, 410151, Hunan, ChinaTechnology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, 100094, ChinaDepartment of Electric Engineering and Information, Hunan University, Changsha, 410082, Hunan, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2211379724007125Convolutional autoencoderSoliton collisionsOptical event horizon |
| spellingShingle | Qibo Xu Jifang Rong Qilin Zeng Xiaofang Yuan Longnv Huang Hua Yang From data to dynamics: Reconstructing soliton collision phenomena in optical fibers using a convolutional autoencoder Results in Physics Convolutional autoencoder Soliton collisions Optical event horizon |
| title | From data to dynamics: Reconstructing soliton collision phenomena in optical fibers using a convolutional autoencoder |
| title_full | From data to dynamics: Reconstructing soliton collision phenomena in optical fibers using a convolutional autoencoder |
| title_fullStr | From data to dynamics: Reconstructing soliton collision phenomena in optical fibers using a convolutional autoencoder |
| title_full_unstemmed | From data to dynamics: Reconstructing soliton collision phenomena in optical fibers using a convolutional autoencoder |
| title_short | From data to dynamics: Reconstructing soliton collision phenomena in optical fibers using a convolutional autoencoder |
| title_sort | from data to dynamics reconstructing soliton collision phenomena in optical fibers using a convolutional autoencoder |
| topic | Convolutional autoencoder Soliton collisions Optical event horizon |
| url | http://www.sciencedirect.com/science/article/pii/S2211379724007125 |
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