PPINN: Parareal physics-informed neural network for time-dependent PDEs

Physics-informed neural networks (PINNs) encode physical conservation laws and prior physical knowledge into the neural networks, ensuring the correct physics is represented accurately while alleviating the need for supervised learning to a great degree (Raissi et al., 2019). While effective for rel...

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Veröffentlicht in:	Computer methods in applied mechanics and engineering Jg. 370; H. C; S. 113250
Hauptverfasser:	Meng, Xuhui, Li, Zhen, Zhang, Dongkun, Karniadakis, George Em
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.10.2020 Elsevier BV Elsevier
Schlagworte:	Burgers equation Conservation laws Convergence Datasets Deep neural network Long-time integration Machine learning Multiscale Neural networks Parallel-in-time Physics PINN Reaction-diffusion equations Time dependence Time integration Training Deep neural network Long-time integration PINN Multiscale Machine learning Parallel-in-time
ISSN:	0045-7825, 1879-2138
Online-Zugang:	Volltext
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