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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Results in physics Jg. 67; S. 108027
Hauptverfasser: Xu, Qibo, Rong, Jifang, Zeng, Qilin, Yuan, Xiaofang, Huang, Longnv, Yang, Hua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.12.2024
Elsevier
Schlagworte:
Convolutional autoencoder
Optical event horizon
Soliton collisions
Soliton collisions
Convolutional autoencoder
Optical event horizon
ISSN:2211-3797, 2211-3797
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:2211-3797
2211-3797
DOI:10.1016/j.rinp.2024.108027