Deep learning method for predicting the complex nonlinear dynamics of passively mode-locked fiber laser

Abstract

The dynamic evolution processes are highly complicated nonlinear dynamic processes in passively mode-locked fiber laser systems. Here, an artificial intelligence (AI) model is employed to predict the complex dynamic processes, which uses the long short-term memory network method, serving as an alternative to the numerical calculation of the nonlinear Schrödinger equation (NLSE). We specifically emphasize the complex evolution processes under different gain saturation energies, comparing the results predicted by the AI model with those simulated by the NLSE. The predicted results of the AI model are in good agreement with the simulated results of NLSE. The root mean square errors of test samples in this study are all below 0.15. Furthermore, with GPU acceleration, the AI model achieves a mean simulation time of 0.452 s for 6000 roundtrips, approximately 2391 times faster than the numerical solution of NLSE executed on a CPU.