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Song, Jin, Zhong, Ming, Karniadakis, George Em, Yan, Zhenya (2024) Two-stage initial-value iterative physics-informed neural networks for simulating solitary waves of nonlinear wave equations. Journal of Computational Physics, 505. 112917 doi:10.1016/j.jcp.2024.112917

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Reference TypeJournal (article/letter/editorial)
TitleTwo-stage initial-value iterative physics-informed neural networks for simulating solitary waves of nonlinear wave equations
JournalJournal of Computational Physics
AuthorsSong, JinAuthor
Zhong, MingAuthor
Karniadakis, George EmAuthor
Yan, ZhenyaAuthor
Year2024 (May)Volume505
PublisherElsevier BV
DOIdoi:10.1016/j.jcp.2024.112917Search in ResearchGate
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Mindat Ref. ID17233203Long-form Identifiermindat:1:5:17233203:3
GUID0
Full ReferenceSong, Jin, Zhong, Ming, Karniadakis, George Em, Yan, Zhenya (2024) Two-stage initial-value iterative physics-informed neural networks for simulating solitary waves of nonlinear wave equations. Journal of Computational Physics, 505. 112917 doi:10.1016/j.jcp.2024.112917
Plain TextSong, Jin, Zhong, Ming, Karniadakis, George Em, Yan, Zhenya (2024) Two-stage initial-value iterative physics-informed neural networks for simulating solitary waves of nonlinear wave equations. Journal of Computational Physics, 505. 112917 doi:10.1016/j.jcp.2024.112917
In(2024) Journal of Computational Physics Vol. 505. Elsevier BV

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Zou, Zongren, Meng, Xuhui, Karniadakis, George Em (2024) Correcting model misspecification in physics-informed neural networks (PINNs) Journal of Computational Physics, 505. 112918 doi:10.1016/j.jcp.2024.112918
Jin, Xiaowei, Cai, Shengze, Li, Hui, Karniadakis, George Em (2021) NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations. Journal of Computational Physics, 426. 109951 doi:10.1016/j.jcp.2020.109951
Zou, Zongren, Karniadakis, George Em (2025) Multi-head physics-informed neural networks for learning functional priors and uncertainty quantification. Journal of Computational Physics, 531. doi:10.1016/j.jcp.2025.113947
Shukla, Khemraj, Jagtap, Ameya D., Karniadakis, George Em (2021) Parallel physics-informed neural networks via domain decomposition. Journal of Computational Physics, 447. 110683 doi:10.1016/j.jcp.2021.110683
Jagtap, Ameya D., Mao, Zhiping, Adams, Nikolaus, Karniadakis, George Em (2022) Physics-informed neural networks for inverse problems in supersonic flows. Journal of Computational Physics, 466. 111402 doi:10.1016/j.jcp.2022.111402
Jagtap, Ameya D., Kawaguchi, Kenji, Karniadakis, George Em (2020) Adaptive activation functions accelerate convergence in deep and physics-informed neural networks. Journal of Computational Physics, 404. 109136 doi:10.1016/j.jcp.2019.109136
Yang, Liu, Meng, Xuhui, Karniadakis, George Em (2021) B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data. Journal of Computational Physics, 425. 109913 doi:10.1016/j.jcp.2020.109913
Lou, Qin, Meng, Xuhui, Karniadakis, George Em (2021) Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation. Journal of Computational Physics, 447. 110676 doi:10.1016/j.jcp.2021.110676
Zhang, Dongkun, Lu, Lu, Guo, Ling, Karniadakis, George Em (2019) Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems. Journal of Computational Physics, 397. 108850 doi:10.1016/j.jcp.2019.07.048
Raissi, M., Perdikaris, P., Karniadakis, G.E. (2019) Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378. 686-707 doi:10.1016/j.jcp.2018.10.045
Howard, Amanda A., Perego, Mauro, Karniadakis, George Em, Stinis, Panos (2023) Multifidelity deep operator networks for data-driven and physics-informed problems. Journal of Computational Physics, 493. 112462 doi:10.1016/j.jcp.2023.112462
 
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