Machine learning method for calculating mode-locking performance of linear cavity fiber lasers

As a commonly used pulse light source, fiber mode-locked lasers are widely used in communication, frequency comb, nonlinear optics and other fields. Although people can accurately predict and analyze the performance of mode-locked pulse with given parameters of fiber laser by existing theories and calculation methods, it is difficult to analyze the mode-locked performance of fiber laser with multi-parameter changes by traditional methods. In this paper, we use machine learning technology to put forward a new view on this problem. Firstly, we use an artificial neural network (ANN) to judge whether a small noise pulse can evolve into a stable mode-locked state in the linear cavity fiber laser. Then, the time domain function of sample pulse is expanded by Fourier series. We use the Fourier coefficients as the network output and train another ANN that can predict the pulse shape quickly and accurately. Finally, we demonstrate how to use the genetic algorithm and the trained network to compute the parameters of the fiber laser when the given pulse width is known. The authors believe that the theoretical ideas and computational models presented in this work have great potential in the dynamics research and fabrication of fiber lasers.

Automated summary

This paper proposes a new approach to analyze the mode-locked performance of fiber lasers using machine learning technology. By using artificial neural networks to judge pulse evolution and predict pulse shape, and combining the genetic algorithm, the authors demonstrate how to compute the parameters of fiber lasers.