Dual feed-forward neural network for predicting complex nonlinear dynamics of mode-locked fiber laser under variable cavity parameters

We propose a dual feed-forward neural network (DFNN) model, consisting of a cavity parameter feature expander (CPFE) and a dynamic process predictor (DPP), for predicting the complex nonlinear dynamics of mode-locked fiber lasers.The output of the CPFE, following layer normalization, is combined with the pulse complex electric field amplitude and then fed into the DPP to predict the dynamics.The pulse evolution process from the detuned steady state to the steady state under different cavity configurations is rapidly calculated.The predicted results of the proposed DFNN are consistent with the numerical split-step Fourier method (SSFM).The simulation speed has been greatly improved with low computational complexity, which is approximately 152 times faster than the SSFM and 4 times faster than the long short-term memory recurrent neural network (LSTM) model.The findings provide a new low computational complexity and efficient machine learning approach to model the complex nonlinear dynamics of mode-locked lasers.