Deep learning neural networks for the third-order nonlinear Schrödinger equation: bright solitons, breathers, and rogue waves

DOI10.1088/1572-9494/ac1cd9zbMath1514.35421arXiv2104.14809MaRDI QIDQ6046358

Zhenya Yan, Zijian Zhou

Publication date: 10 May 2023

Published in: Communications in Theoretical Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2104.14809

zbMATH Keywords

deep learningdata-driven solitonsdata-driven parameter discoverythird-order nonlinear Schrödinger equation

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) NLS equations (nonlinear Schrödinger equations) (35Q55) Soliton solutions (35C08) Time-dependent Schrödinger equations and Dirac equations (35Q41)

Related Items (6)

Data-driven rogue waves and parameters discovery in nearly integrable \(\mathcal{PT}\)-symmetric Gross-Pitaevskii equations via PINNs deep learning ⋮ EXPLICIT OPTICAL DROMIONS WITH KERR LAW HAVING FRACTIONAL TEMPORAL EVOLUTION ⋮ Deep neural networks learning forward and inverse problems of two-dimensional nonlinear wave equations with rational solitons ⋮ Data-driven discoveries of Bäcklund transformations and soliton evolution equations via deep neural network learning schemes ⋮ Higher-order rogue waves solutions of the modified Gerdjikov-Ivanov equation with dispersion via generalized Darboux transformation ⋮ Data-driven prediction of soliton solutions of the higher-order NLSE via the strongly-constrained PINN method

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