Predicting nonlinear dynamics of optical solitons in optical fiber via the SCPINN

TL;DR

This paper introduces SCPINN, a physics-informed neural network that accurately predicts the nonlinear dynamics of optical solitons in fibers, significantly outperforming traditional PINN methods.

Contribution

The paper proposes SCPINN, which incorporates compound derivatives and adaptive weighting to enhance the prediction accuracy of soliton dynamics in optical fibers.

Findings

SCPINN improves prediction accuracy by 5-11 times over PINN.

It effectively models the formation and evolution of various optical solitons.

The method reveals detailed physical quantity variations during soliton transmission.

Abstract

The strongly-constrained physics-informed neural network (SCPINN) is proposed by adding the information of compound derivative embedded into the soft-constraint of physics-informed neural network(PINN). It is used to predict nonlinear dynamics and the formation process of bright and dark picosecond optical solitons, and femtosecond soliton molecule in the single-mode fiber, and reveal the variation of physical quantities including the energy, amplitude, spectrum and phase of pulses during the soliton transmission. The adaptive weight is introduced to accelerate the convergence of loss function in this new neural network. Compared with the PINN, the accuracy of SCPINN in predicting soliton dynamics is improved by 5-11 times. Therefore, the SCPINN is a forward-looking method to study the modeling and analysis of soliton dynamics in the fiber.