A Fast Prediction Framework for Multi-Variable Nonlinear Dynamic Modeling of Fiber Pulse Propagation Using DeepONet

Abstract

Traditional femtosecond laser modeling relies on the iterative solution of the Nonlinear Schrödinger Equation (NLSE) using the Split-Step Fourier Method (SSFM). However, SSFM’s high computational complexity leads to significant time consumption, particularly in automatic control and system optimization, thus limiting control model responsiveness. Recent studies have suggested using neural networks to simulate fiber dynamics, offering faster computation and lower costs. In this study, we introduce a novel fiber propagation method utilizing the DeepONet architecture for the first time. By separately managing fiber parameters and input–output pulses in the branch and trunk networks, this method can simulate various fiber configurations with high accuracy and without altering the architecture. Additionally, while SSFM generation time increases linearly with fiber length, the GPU-accelerated AI generation time remains consistent at around 0.0014 s, regardless of length. Notably, in high-order soliton (HOS) compression over a 12 m distance, the AI method is approximately 56,865 times faster than SSFM.

Keywords

• DeepONet
• fiber pulse propagation
• nonlinear dynamics
• neural networks
• femtosecond laser
• Split-Step Fourier Method