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- Title
Prediction of self-similar waves in tapered graded index diffraction decreasing waveguide by the A-gPINN method.
- Authors
Li, Lang; Qiu, Weixin; Dai, Chaoqing; Wang, Yueyue
- Abstract
In this paper, an adaptive gradient-enhanced physics-informed neural network method(A-gPINN) is proposed to investigate the dynamics of solitons in tapered refractive index waveguides. A-gPINN method adopts adaptive sampling and incorporates the gradient information of the nonlinear partial differential equation into the neural network. Compared to traditional methods, A-gPINN can achieve a more accurate prediction of complicated soliton structures in a larger computational domain with less training data. Using this method, the evolution of self-similar bright solitons, self-similar soliton pairs, self-similar rogue waves, and self-similar Akhmediev breathers has been successfully and accurately predicted, while the coefficient variations of the generalized non-homogeneous nonlinear Schrödinger equation have been predicted reversely. Due to the superiority of this method, it turns to be a promising neural network method for studying soliton dynamics in optical fibers, and it also has application potential in other physical fields such as nonlinear optics and Bose Einstein condensation.
- Subjects
BOSE-Einstein condensation; SOLITONS; ROGUE waves; PARTIAL differential equations; NONLINEAR differential equations; NONLINEAR optics; WAVEGUIDES; NONLINEAR Schrodinger equation
- Publication
Nonlinear Dynamics, 2024, Vol 112, Issue 12, p10319
- ISSN
0924-090X
- Publication type
Article
- DOI
10.1007/s11071-024-09608-6