On prediction of chaotic dynamics in semiconductor lasers by reservoir computing.

Studying the chaotic dynamics of semiconductor lasers is of great importance for their applications in random bit generation and secure communication. While considerable effort has been expended towards investigating these chaotic behaviors through numerical simulations and experiments, the accurate prediction of chaotic dynamics from limited observational data remains a challenge. Recent advancements in machine learning, particularly in reservoir computing, have shown promise in capturing and predicting the complex dynamics of semiconductor lasers. However, existing works on laser chaos predictions often suffer from the need for manual parameter optimization. Moreover, the generalizability of the approach remains to be investigated, i.e., concerning the influences of practical laser inherent noise and measurement noise. To address these challenges, we employ an automated optimization approach, i.e., a genetic algorithm, to select optimal reservoir parameters. This allows efficient training of the reservoir network, enabling the prediction of continuous intensity time series and reconstruction of laser dynamics. Furthermore, the impact of inherent laser noise and measurement noise on the prediction of chaotic dynamics is systematically examined through numerical analysis. Simulation results demonstrate the effectiveness and generalizability of the proposed approach in achieving accurate predictions of chaotic dynamics in semiconductor lasers.