  With a great ability to solve regression problems, the artificial neural
network has become a powerful tool to facilitate advancing ultrafast laser
research. In this contribution, we demonstrate the capability of a feed-forward
neural network (FNN) to predict the output parameters of a mode-locked fiber
laser, which mutually depend on multiple intracavity parameters, with high
speed and accuracy. A direct mapping between cavity parameters and laser output
is realized through the FNN-trained models, bypassing tedious iterative
numerical simulation as a common approach to get a converged solution for a
laser cavity. We show that the laser output spectrum and temporal pulse
profiles can be accurately predicted with the normalized root mean square error
(NRMSE) of less than 0.032 within only a 5 ms time frame for scenarios inside
and outside the training data. We investigate the influence of FNN
configuration on prediction performance. Both gain and spectral filter
parameters are explored to test the prediction capability of the trained FNN
models at high speed. Straightforward and fast prediction of the laser output
performance for varying laser intracavity parameters paves the way to
intelligent short-pulsed lasers with inversed design or autonomous operation
maintenance.
