Data-driven prediction of soliton solutions of the higher-order NLSE via the strongly-constrained PINN method

DOI10.1016/j.camwa.2022.09.025OpenAlexW4306182260MaRDI QIDQ2107164

Yanyan Li

Publication date: 1 December 2022

Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.camwa.2022.09.025

zbMATH Keywords

higher-order nonlinear Schrödinger equationsoliton solutionsoliton dynamicsstrongly-constrained physics-informed neural network

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) NLS equations (nonlinear Schrödinger equations) (35Q55)

Uses Software

• XPINNs

Cites Work

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