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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Vydané v:	Computer methods in applied mechanics and engineering Ročník 370; číslo C; s. 113250
Hlavní autori:	Meng, Xuhui, Li, Zhen, Zhang, Dongkun, Karniadakis, George Em
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 01.10.2020 Elsevier BV Elsevier
Predmet:	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
On-line prístup:	Získať plný text
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