Predicting certain vector optical solitons via the conservation-law deep-learning method

Y Fang, GZ Wu, XK Wen, YY Wang, CQ Dai - Optics & Laser Technology, 2022 - Elsevier
The energy conservation law is introduced into a loss function of the physics-informed
neural network (PINN), and an energy-conservation deep-learning (ECDL) method is …
被引用次数：81 相关文章 所有 2 个版本
[PDF] arxiv.org

Predicting the dynamic process and model parameters of the vector optical solitons in birefringent fibers via the modified PINN

GZ Wu, Y Fang, YY Wang, GC Wu, CQ Dai - Chaos, Solitons & Fractals, 2021 - Elsevier
A modified physics-informed neural network is used to predict the dynamics of optical pulses
including one-soliton, two-soliton, and rogue wave based on the coupled nonlinear …
被引用次数：62 相关文章 所有 9 个版本

Predicting the dynamic process and model parameters of vector optical solitons under coupled higher-order effects via WL-tsPINN

BW Zhu, Y Fang, W Liu, CQ Dai - Chaos, Solitons & Fractals, 2022 - Elsevier
We propose the two-subnet physical information neural network with the weighted loss
function (WL-tsPINN) to study the higher-order effects of ultra-short pulses in birefringence …
被引用次数：22 相关文章 所有 4 个版本

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

GZ Wu, Y Fang, NA Kudryashov, YY Wang… - Chaos, Solitons & …, 2022 - Elsevier
In this work, based on the original physics-informed neural networks, we propose an
improved physics-informed neural network method by combining the conservation laws. As …
被引用次数：30 相关文章 所有 5 个版本

Abundant vector soliton prediction and model parameter discovery of the coupled mixed derivative nonlinear Schrödinger equation

XK Wen, JH Jiang, W Liu, CQ Dai - Nonlinear Dynamics, 2023 - Springer
Using the extended physics-informed neural network with twin subnets to study the coupled
mixed derivative nonlinear Schrödinger equation (NLSE), seven types of vector solitons …
被引用次数：65 相关文章 所有 2 个版本
[PDF] arxiv.org

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

Y Fang, WB Bo, RR Wang, YY Wang, CQ Dai - Chaos, Solitons & Fractals, 2022 - Elsevier
The strongly-constrained physics-informed neural network (SCPINN) is proposed by adding
the information of compound derivative embedded into the soft-constraint of physics …
被引用次数：17 相关文章 所有 7 个版本
[PDF] arxiv.org

Data-driven femtosecond optical soliton excitations and parameters discovery of the high-order NLSE using the PINN

Y Fang, GZ Wu, YY Wang, CQ Dai - Nonlinear Dynamics, 2021 - Springer
We use the physics-informed neural network to solve a variety of femtosecond optical soliton
solutions of the high-order nonlinear Schrödinger equation, including one-soliton solution …
被引用次数：184 相关文章 所有 6 个版本

Dynamic analysis on optical pulses via modified PINNs: Soliton solutions, rogue waves and parameter discovery of the CQ-NLSE

YH Yin, X Lü - Communications in Nonlinear Science and Numerical …, 2023 - Elsevier
Under investigation in this paper is the cubic–quintic nonlinear Schrödinger equation, which
describes the propagation of optical on resonant-frequency fields in the inhomogeneous …
被引用次数：53 相关文章 所有 2 个版本

Data-driven solitons and parameter discovery to the (2+ 1)-dimensional NLSE in optical fiber communications

X Peng, YW Zhao, X Lü - Nonlinear Dynamics, 2024 - Springer
In this paper, we investigate the (2+ 1)-dimensional nonlinear Schrödinger equation (NLSE)
which characterizes the transmission of optical pulses through optical fibers exhibiting …
被引用次数：33 相关文章

Dynamics of diverse data-driven solitons for the three-component coupled nonlinear Schrödinger model by the MPS-PINN method

XK Wen, GZ Wu, W Liu, CQ Dai - Nonlinear Dynamics, 2022 - Springer
We improve the physical information neural network by adding multiple parallel subnets to
predict seven types of soliton dynamics, such as one soliton, two solitons and soliton …
被引用次数：49 相关文章 所有 3 个版本