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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Bibliographic Details
Published in:	Computer methods in applied mechanics and engineering Vol. 370; no. C; p. 113250
Main Authors:	Meng, Xuhui, Li, Zhen, Zhang, Dongkun, Karniadakis, George Em
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.10.2020 Elsevier BV Elsevier
Subjects:	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 Access:	Get full text
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