2023/06/14 by Andrei Ermolaev, Ermolaev, Andrei V., Mehdi Mabed +7
Engineering · Physics and Astronomy · #Advanced Fiber Laser Technologies #FOS: Physical sciences #Optics (physics.optics) #Pattern Formation and Solitons (nlin.PS) #Photonic Crystal and Fiber Optics #Photonic and Optical Devices
paper · pdf · doi:10.48550/arxiv.2306.11888
openalex publication_date 2023/06/14 · openalex created_date 2023/06/24 · openalex updated_date 2026/07/28
We analyze the dynamics of modulation instability in optical fiber (or any other nonlinear Schrödinger equation system) using the machine-learning technique of data-driven dominant balance. We aim to automate the identification of which particular physical processes drive propagation in different regimes, a task usually performed using intuition and comparison with asymptotic limits. We first apply the method to interpret known analytic results describing Akhmediev breather, Kuznetsov-Ma, and Peregrine soliton (rogue wave) structures, and show how we can automatically distinguish regions of dominant nonlinear propagation from regions where nonlinearity and dispersion combine to drive the observed spatio-temporal localization. Using numerical simulations, we then apply the technique to the more complex case of noise-driven spontaneous modulation instability, and show that we can readily isolate different regimes of dominant physical interactions, even within the dynamics of chaotic propagation.