Predicting behavior of photonic crystal fiber lasers using artificial neural networks

Simulating pulse propagation within fiber lasers is a difficult and computationally demanding task, which limits the ability to accurately capture their complex nonlinear dynamics. This paper leverages artificial intelligence (AI) and artificial neural networks (ANN) to predict the output pulse shape parameters for a ring cavity fiber laser, thus avoiding the need for traditional complex numerical calculations and reducing the overall computational cost. This study focuses on a dissipative soliton resonance (DSR) ring cavity fiber laser, incorporating a photonic crystal fiber (PCF) and mode locked by a real saturable absorber. The integration of the PCF into the laser cavity design enhances the control of both dispersion and nonlinearity, allowing for a greater range of output pulse shapes and parameters. The proposed approach demonstrates the effectiveness of artificial neural networks in predicting pulse parameters, and could pave the way for more efficient design and optimization of fiber lasers. The results of the study show that the ANN model can accurately predict the output pulse shape parameters for the DSR ring cavity fiber laser, and thus offers a promising tool for future research in the field.