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As an imperative method of investigating the internal mechanism offemtosecond lasers, traditional femtosecond laser modeling relies on thesplit-step Fourier method (SSFM) to iteratively resolve the nonlinearSchrodinger equation suffering from the large computation complexity. Torealize inverse design and optimization of femtosecond lasers, numeroussimulations of mode-locked fiber lasers with different cavity settings arerequired further highlighting the time-consuming problem induced by the largecomputation complexity. Here, a recurrent neural network is proposed to realizefast and accurate femtosecond mode-locked fiber laser modeling for the firsttime. The generalization over different cavity settings is achieved via ourproposed prior information feeding method. With the acceleration of GPU, themean time of the artificial intelligence (AI) model inferring 500 roundtrips isless than 0.1 s. Even on an identical CPU-based hardware platform, the AI modelis still 6 times faster than the SSFM method. The proposed AI-enabled method ispromising to become a standard approach to femtosecond laser modeling.

Fast predicting the complex nonlinear dynamics of mode-locked fiber laser by a recurrent neural network with prior information feeding