We present the integration of an automated polarization control system into a figure-eight fiber laser with the aim of self-tuning noise-like pulses (NLPs). The system optimizes polarization adjustments by using adaptive control and predictive neural networks (NNs), enhancing temporal and spectral behavior. This approach enables precise control over pulse characteristics, achieving an average output power of 275.25 mW (302.8 nJ) for signal emission at ~1567 nm; adjustable NLP envelope durations from 13 ns to 48 ns, corresponding to spectral widths from 50 to more than 200 nm; and the ability to increase in a controllable way the repetition frequency up to 100 times the fundamental frequency, which corresponds to 909 kHz, through cavity harmonic pulse generation. A multiparameter pulsed regime-seeking algorithm stabilizes high-energy NLPs in the fundamental or harmonic regime while predictive networks optimize the cavity response, and the process is completed in an average time of 11.5 s. The automated polarization control system enables high cavity harmonic pulse generation, as well as broadband supercontinuum (SC) spectrum with high flatness.
Tapia-Garcia et al. (2026) studied this question.