SRS-Net: a universal framework for solving stimulated Raman scattering in nonlinear fiber-optic systems by...

Area of Science:

• Photonics and Nonlinear Optics
• Computational Physics
• Machine Learning Applications

Background:

• Stimulated Raman scattering (SRS) is a critical nonlinear phenomenon in fiber optics, impacting performance and enabling devices like Raman amplifiers.
• Existing numerical methods for SRS are often time-consuming, inefficient, and problem-specific, especially for combined forward and inverse scenarios.

Purpose of the Study:

• To develop a universal and efficient solution for stimulated Raman scattering (SRS) problems, including forward, inverse, and combined scenarios.
• To leverage neural networks and physical laws for a more robust and faster approach to solving SRS.

Main Methods:

• Introduction of SRS-Net, a physics-informed neural network integrating automatic differentiation and neural network capabilities.
• Incorporation of SRS physical laws as regularization within the neural network framework.
• Extensive simulations and experimental validation for diverse SRS scenarios.

Main Results:

• SRS-Net demonstrates intuitive solving procedures and high-speed performance across various SRS scenarios.
• Successful high-fidelity modeling of a C+L-band wavelength division multiplexing system (approx. 10 THz).
• Validation of the framework's versatility beyond SRS for other nonlinear dynamics problems.

Conclusions:

• SRS-Net provides a universal, efficient, and high-speed solution for complex stimulated Raman scattering problems.
• The framework shows significant potential for application in other engineering fields governed by nonlinear partial differential equations.