Data-driven prediction of spatial optical solitons in fractional diffraction
Chaos, Solitons & Fractals, ISSN: 0960-0779, Vol: 175, Page: 114085
2023
- 4Citations
- 8Captures
- 1Mentions
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Article Description
A quasi-residual physics-informed neural network (QR_PINN) with efficient residual-like blocks, was investigated based on classical physics-informed neural network to solve nonlinear fractional Schrödinger equation and analyze the transmission of spatial optical solitons in saturable nonlinear media with fractional diffraction. A comprehensive verification of stable transmission of various solitons under PT-symmetric potential was carried out using the QR_PINN. In addition, the transmission of spatial optical solitons was studied under simple real potential (stable transmission) and complex Scarf-II potential (unstable transmission). The results show that the QR_PINN can accurately reconstruct the transmission of spatial optical solitons under fractional diffraction. Meanwhile, as the complexity of the potential function increases, the prediction accuracy of the QR_PINN slightly decreases. These results provide a new approach for the application of deep learning in the nonlinear fractional Schrödinger equation.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0960077923009864; http://dx.doi.org/10.1016/j.chaos.2023.114085; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85172371123&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0960077923009864; https://dx.doi.org/10.1016/j.chaos.2023.114085
Elsevier BV
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