Fiber laser development enabled by machine learning: review and prospect
Min Jiang, Hanshuo Wu, Yi An, Tianyue Hou, Qi Chang, Liangjin Huang, Jun Li, Rongtao Su, Pu Zhou
Abstract
Abstract In recent years, machine learning, especially various deep neural networks, as an emerging technique for data analysis and processing, has brought novel insights into the development of fiber lasers, in particular complex, dynamical, or disturbance-sensitive fiber laser systems. This paper highlights recent attractive research that adopted machine learning in the fiber laser field, including design and manipulation for on-demand laser output, prediction and control of nonlinear effects, reconstruction and evaluation of laser properties, as well as robust control for lasers and laser systems. We also comment on the challenges and potential future development.