Data driven localized wave solution of the Fokas-Lenells equation using modified PINN

Gautam Kumar Saharia, Sagardeep Talukdar, Riki Dutta, Sudipta Nandy
6/3/2023
nlin.PScs.LGnlin.SI

Official code: gautamksaharia/Fokas-Lenells (0 stars, GPL-3.0, last commit 2025-09-07).

Abstract

We investigate data driven localized wave solutions of the Fokas-Lenells equation by using physics informed neural network(PINN). We improve basic PINN by incorporating control parameters into the residual loss function. We also add conserve quantity as another loss term to modify the PINN. Using modified PINN we obtain the data driven bright soliton and dark soliton solutions of Fokas-Lenells equation. Conserved quantities informed loss function achieve more accuracy in terms of relative L2 error between predicted and exact soliton solutions. We hope that the present investigation would be useful to study the applications of deep learning in nonlinear optics and other branches of nonlinear physics. Source codes are available at https://github.com/gautamksaharia/Fokas-Lenells

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Code Implementations(1)

gautamksaharia/Fokas-LenellsOfficial100%
GPL-3.0

Physics informed neural network (PINN) implementation to solve Fokas-Lenells equation

01Python, Jupyter NotebookMay 19, 20231 years agoGPL-3.0

Cite this paper

@article{saharia2023data,
  title  = {Data driven localized wave solution of the Fokas-Lenells equation using modified PINN},
  author = {Gautam Kumar Saharia and Sagardeep Talukdar and Riki Dutta and Sudipta Nandy},
  year   = {2023},
  eprint = {2306.03105},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.03105v1}
}

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