A Deep Learning Approach for Dynamic Modeling of Stimulated Raman Scattering in Chalcogenide Microstructured Optical...

Stimulated Raman scattering (SRS) plays a pivotal role in applications such as optical communications, fiber optic sensing, and spectral analysis. However, traditional modeling methods like the split-step Fourier method (SSFM) are computationally demanding. In response to these challenges, we propose a novel deep learning framework based on a hybrid neural network, specifically architected to capture the complex spatio-temporal dependencies inherent in nonlinear pulse propagation. Our model offers rapid and precise predictions of SRS behavior, alleviating the need for computationally expensive simulations like SSFM. To validate the model's performance, we conducted experiments using chalcogenide microstructured optical fibers (MOFs), which are attracting attention due to their high Raman gain coefficient and wide spectral range in the mid-infrared (MIR) region. Specifically, we demonstrate the first successful generation of MIR SRS in a 2- $\mu $ m direct-pumped suspended-core As₂S₃ MOF, which provides a crucial real-world dataset for model validation. The results demonstrate that our hybrid neural network is 116 times faster on a GPU and 44 times faster on a CPU compared to SSFM while maintaining accuracy and generalization. This significant acceleration paves the way for real-time analysis and inverse design of nonlinear photonic systems, tasks previously intractable with traditional methods.