Prediction of optical solitons using an improved physics-informed neural network method with the conservation law constraint

DOI10.1016/j.chaos.2022.112143zbMath1505.78035OpenAlexW4225269297MaRDI QIDQ2113135

Gang-Zhou Wu, Nikolay A. Kudryashov, Yue-Yue Wang, Chao-Qing Dai, Yin Fang

Publication date: 12 January 2023

Published in: Chaos, Solitons and Fractals (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.chaos.2022.112143

zbMATH Keywords

conservation lawsoptical solitonsimproved physics-informed neural networkstandard nonlinear Schrödinger equation

Mathematics Subject Classification ID

NLS equations (nonlinear Schrödinger equations) (35Q55) Lasers, masers, optical bistability, nonlinear optics (78A60)

Related Items (3)

Spatiotemporal dynamics on a class of \((n+1)\)-dimensional reaction-diffusion neural networks with discrete delays and a conical structure ⋮ A dimension-augmented physics-informed neural network (DaPINN) with high level accuracy and efficiency ⋮ Data-driven prediction of soliton solutions of the higher-order NLSE via the strongly-constrained PINN method

Uses Software

• ImageNet
• AlexNet
• FPINNs
• PPINN
• NSFnets

Cites Work

• Conservation laws, bilinear forms and solitons for a fifth-order nonlinear Schrödinger equation for the attosecond pulses in an optical fiber
• Conservation laws, soliton solutions and modulational instability for the higher-order dispersive nonlinear Schrödinger equation
• PPINN: parareal physics-informed neural network for time-dependent PDEs
• \textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition
• Data-driven soliton solutions and model parameters of nonlinear wave models via the conservation-law constrained neural network method
• NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations
• Predicting the dynamic process and model parameters of the vector optical solitons in birefringent fibers \textit{via} the modified PINN
• Meyer wavelet neural networks to solve a novel design of fractional order pantograph Lane-Emden differential model
• Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems
• Data-driven rogue waves and parameter discovery in the defocusing nonlinear Schrödinger equation with a potential using the PINN deep learning
• Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
• fPINNs: Fractional Physics-Informed Neural Networks
• A deep learning method for solving third-order nonlinear evolution equations

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