The data-driven rogue waves of the Hirota equation by using Mix-training PINNs approach
Author:
Funder
Ningbo University
National Natural Science Foundation of China
Publisher
Elsevier BV
Reference27 articles.
1. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations;Raissi;J. Comput. Phys.,2019
2. fPINNs: Fractional physics-informed neural networks;Pang;SIAM J. Sci. Comput.,2019
3. Physics-informed neural networks for inverse problems in supersonic flows;Jagtap;J. Comput. Phys.,2022
4. Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems;Zhang;J. Comput. Phys.,2019
5. Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations;Moseley,2021
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