Rethinking deep learning for supercontinuum: Efficient modeling based on integrated and compressed networks

To accurately predict the complex dynamical processes of supercontinuum generation in optical fibers, an integrated deep learning model was constructed in this study, fully incorporating the strengths of bidirectional Long Short-Term Memory, Gated Recurrent Units, and Fully Connected Networks. Superior prediction precision was achieved by the integrated model in both time and frequency domains. However, the large number of parameters in the integrated model makes it unfavorable for practical deployment. To address this issue, knowledge distillation was employed, where the pre-trained integrated model guides the learning of a lightweight model. The results demonstrate that compared to standalone Long Short-Term Memory and the undistilled lightweight model, the distilled lightweight model maintains high prediction accuracy and generalization capability while significantly reducing model complexity, making it better suited for deployment on hardware systems. This research marks the first time that integrated learning and knowledge distillation are applied to predict supercontinuum generation in optical fibers, successfully striking a balance between precision and efficiency. This research provides important guidance for deep learning applications in the field of nonlinear dynamics.