Data-driven optical parameter identification for the Ginzburg–Landau equation via Bayesian methods

This paper leverages the Ginzburg–Landau equation, a fundamental model for elucidating the dynamics of dissipative optical solitons, in conjunction with the Markov Chain Monte Carlo method and the Lilliefors normality test, to precisely identify key parameters such as dispersion coefficient, nonline...

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Bibliographic Details
Published in:Optical and quantum electronics 2024-08, Vol.56 (8), Article 1393
Main Authors: Zhao, Yedan, Xu, Yinghong, Zhang, Lipu, Chen, Changdi
Format: Article
Language:English
Subjects:
Bayesian analysis
Characterization and Evaluation of Materials
Communications systems
Computer Communication Networks
Conditional probability
Confidence intervals
Design optimization
Design parameters
Electrical Engineering
Fiber lasers
Fiber optics
Landau-Ginzburg equations
Lasers
Markov chains
Monte Carlo simulation
Normality
Optical Devices
Optics
Parameter estimation
Parameter identification
Photonics
Physics
Physics and Astronomy
Probability density functions
Solitary waves
Statistical analysis
Citations: Items that this one cites
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Summary:This paper leverages the Ginzburg–Landau equation, a fundamental model for elucidating the dynamics of dissipative optical solitons, in conjunction with the Markov Chain Monte Carlo method and the Lilliefors normality test, to precisely identify key parameters such as dispersion coefficient, nonlinear coefficient, and gain coefficient. Our comprehensive analysis yields not only posterior mean estimates and confidence intervals for these parameters but also their corresponding posterior probability density functions. These findings offer valuable statistical insights and theoretical underpinnings for the design and optimization of fiber lasers, with implications for enhancing the performance of fiber-optic communication systems.
ISSN:1572-817X
0306-8919
1572-817X
DOI:10.1007/s11082-024-07330-6