Machine learning analysis of instabilities in noise-like pulse lasers

Analysis of interaction dynamics and rogue wave localization in modulation instability using data-driven dominant balance

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.

Nonlinear spectral tunability of pulsed fiber laser with semiconductor optical amplifier

We examine spectral properties of radiation in the pulsed fiber lasers using the semiconductor optical amplifier (SOA) as the gain medium. The complex light dynamics that result from the interplay between the fiber propagation effects in the cavity, the nonlinear effects in the SOA and spectral filtering, shift the generated radiation from the central wavelength of the filter. The resulting wavelength of the output radiation depends on the SOA pump power and the bandwidth of the intracavity filter. This offers the possibility of a spectral tunability of the generated pulses through nonlinear dynamics rather than the conventional use of a tunable filter.