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, 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.