Intelligent breathing soliton generation in ultrafast fibre lasers

TL;DR

This paper demonstrates the use of an evolutionary algorithm to autonomously generate and control breathing soliton regimes in ultrafast fibre lasers, enabling tailored nonlinear pulse dynamics.

Contribution

It introduces an evolutionary optimization approach for self-tuning complex breathing soliton states in fibre lasers, expanding the capabilities of machine learning in dynamic pulse regime control.

Findings

Successfully generated various breathing soliton states

Controlled oscillation periods and breathing ratios

Created breather molecular complexes with adjustable constituents

Abstract

Harnessing pulse generation from an ultrafast laser is a challenging task as reaching a specific mode-locked regime generally involves adjusting multiple control parameters, in connection with a wide range of accessible pulse dynamics. Machine-learning tools have recently shown promising for the design of smart lasers that can tune themselves to desired operating states. Yet, machine-learning algorithms are mainly designed to target regimes of parameter-invariant, stationary pulse generation, while the intelligent excitation of evolving pulse patterns in a laser remains largely unexplored. Breathing solitons exhibiting periodic oscillatory behavior, emerging as ubiquitous mode-locked regime of ultrafast fibre lasers, are attracting considerable interest by virtue of their connection with a range of important nonlinear dynamics, such as exceptional points, and the Fermi-Pasta-Ulam paradox. Here, we implement an evolutionary algorithm for the self-optimisation of the breather regime in a fibre laser mode-locked through a four-parameter nonlinear polarisation evolution. Depending on the specifications of the merit function used for the optimisation procedure, various breathing-soliton states are obtained, including single breathers with controllable oscillation period and breathing ratio, and breather molecular complexes with a controllable number of elementary constituents. Our work opens up a novel avenue for exploration and optimisation of complex dynamics in nonlinear systems.