Data-driven soliton mappings for integrable fractional nonlinear wave equations via deep learning with Fourier neural operator

@article{Zhong2022DatadrivenSM,
  title={Data-driven soliton mappings for integrable fractional nonlinear wave equations via deep learning with Fourier neural operator},
  author={Ming Zhong and Zhenya Yan},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.14291}
}
: In this paper, we firstly extend the Fourier neural operator (FNO) to discovery the soliton mapping between two function spaces, where one is the fractional-order index space { ǫ | ǫ ∈ ( 0, 1 ) } in the fractional integrable nonlinear wave equations while another denotes the solitonic solution function space. To be specific, the fractional nonlinear Schr¨odinger (fNLS), fractional Korteweg-de Vries (fKdV), fractional modified Korteweg-de Vries (fmKdV) and fractional sine-Gordon (fsineG… 
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