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Prediction of self-similar waves in tapered graded index diffraction decreasing waveguide by the A-gPINN method.
- Source :
- Nonlinear Dynamics; Jun2024, Vol. 112 Issue 12, p10319-10340, 22p
- Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0924090X
- Volume :
- 112
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- Nonlinear Dynamics
- Publication Type :
- Academic Journal
- Accession number :
- 177538045
- Full Text :
- https://doi.org/10.1007/s11071-024-09608-6