Intelligent Breathing Soliton Generation in Ultrafast Fiber Lasers

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 fiber 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, an evolutionary algorithm is implemented for the self‐optimization of the breather regime in a fiber laser mode‐locked through a four‐parameter nonlinear polarization evolution. Depending on the specifications of the merit function used for the optimization 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. This work opens up a novel avenue for exploration and optimization of complex dynamics in nonlinear systems.