SRS-Net: a universal framework for solving stimulated Raman scattering in nonlinear fiber-optic systems by physics-informed deep learning

As a crucial nonlinear phenomenon, stimulated Raman scattering (SRS) plays multifaceted roles involved in forward and inverse problems. In fibre-optic systems, these roles range from detrimental interference that impairs optical performance to beneficial effects that enables various devices such as Raman amplifier. To obtain solutions of SRS, various numerical methods customized for different scenarios have been proposed. However, these methods are time-consuming, low-efficiency, and experience-orientated, particularly in combined scenarios consisting of both forward and inverse problems. Inspired by physics-informed neural networks, we propose SRS-Net, which combines the efficient automatic differentiation and powerful representation ability of neural networks with the regularization of SRS physical laws, to obtain universal solutions for SRS of forward, inverse, and combined problems. We showcase the intuitive solving procedure and high-speed performance of SRS-Net through extensive simulations covering different scenarios. Additionally, we validate its capabilities in experiments involving the high-fidelity modelling of a wavelength division multiplexing system spanning the C + L-band with approximately 10 THz. The versatility of the SRS-Net framework extends beyond SRS, indicating its potential as a promising universal solution in other engineering problems with nonlinear dynamics governed by partial differential equations. Yuchen Song and colleagues develop a neural network-based framework for solving both forward and inverse problems of stimulated Raman scattering. This physics-informed framework called SRS-Net helps wideband power prediction, Raman pump optimization, and physical parameter identification in fibre optics.