Machine Learning Methods for Control of Fibre Lasers with Double Gain Nonlinear Loop Mirror

Scientific Reports
Alexey KokhanovskiySergey Turitsyn

Abstract

Many types of modern lasers feature nonlinear properties, which makes controlling their operation a challenging engineering problem. In particular, fibre lasers present both high-performance devices that are already used for diverse industrial applications, but also interesting and not yet fully understood nonlinear systems. Fibre laser systems operating at high power often have multiple equilibrium states, and this produces complications with the reproducibility and management of such devices. Self-tuning and feedback-enabled machine learning approaches might define a new era in laser science and technology. The present study is the first to demonstrate experimentally the application of machine learning algorithms for control of the pulsed regimes in an all-normal dispersion, figure-eight fibre laser with two independent amplifying fibre loops. The ability to control the laser operation state by electronically varying two drive currents makes this scheme particularly attractive for implementing machine learning approaches. The self-tuning adjustment of two independent gain levels in the laser cavity enables generation-on-demand pulses with different duration, energy, spectral characteristics and time coherence. We introduce an...Continue Reading

Citations

Jul 2, 2019·Optics Letters·Alexey KokhanovskiySergey Turitsyn
Sep 13, 2019·Optics Express·Ippei NakamuraTakeshi Fukuhara
Mar 7, 2020·Light, Science & Applications·Fanchao Meng, John M Dudley
Feb 27, 2021·Light, Science & Applications·Fanchao Meng, John M Dudley
Jun 3, 2021·Sensors·Igor KudelinMaria Chernysheva
Jul 2, 2021·Scientific Reports·Alexey KokhanovskiySergey Turitsyn

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