Machine learning analysis of instabilities in noise-like pulse lasers

Mehdi Mabed, Fanchao Meng, Lauri Salmela, Christophe Finot, Goëry Genty, John M. Dudley

Research output: Contribution to journalArticlepeer-review


Neural networks have been recently shown to be highly effective in predicting time-domain properties of optical fiber instabilities based only on analyzing spectral intensity profiles. Specifically, from only spectral intensity data, a suitably trained neural network can predict temporal soliton characteristics in supercontinuum generation, as well as the presence of temporal peaks in modulation instability satisfying rogue wave criteria. Here, we extend these previous studies of machine learning prediction for single-pass fiber propagation instabilities to the more complex case of noise-like pulse dynamics in a dissipative soliton laser. Using numerical simulations of highly chaotic behaviour in a noise-like pulse laser operating around 1550 nm, we generate large ensembles of spectral and temporal data for different regimes of operation, from relatively narrowband laser spectra of 70 nm bandwidth at the -20 dB level, to broadband supercontinuum spectra spanning 200 nm at the -20 dB level and with dispersive wave and long wavelength Raman extension spanning from 1150–1700 nm. Using supervised learning techniques, a trained neural network is shown to be able to accurately correlate spectral intensity profiles with time-domain intensity peaks and to reproduce the associated temporal intensity probability distributions.

Original languageEnglish
Pages (from-to)15060-15072
Number of pages13
JournalOptics Express
Issue number9
Publication statusPublished - 25 Apr 2022
Publication typeA1 Journal article-refereed

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics


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