PhyCRNet: Physics-informed convolutional-recurrent network for solving spatiotemporal PDEs

Partial differential equations (PDEs) play a fundamental role in modeling and simulating problems across a wide range of disciplines. Recent advances in deep learning have shown the great potential of physics-informed neural networks (PINNs) to solve PDEs as a basis for data-driven modeling and inve...

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Vydáno v:	Computer methods in applied mechanics and engineering Ročník 389; s. 114399
Hlavní autoři:	Ren, Pu, Rao, Chengping, Liu, Yang, Wang, Jian-Xun, Sun, Hao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier B.V 01.02.2022 Elsevier BV
Témata:	Boundary conditions Burgers equation Coders Convolutional-recurrent learning Deep learning Encoder–decoder Feature extraction Hard-encoding of I/BCs Machine learning Mathematical models Modelling Neural networks Partial differential equations Physics Physics-informed deep learning Residual connection Time marching Encoder–decoder Residual connection Convolutional-recurrent learning Partial differential equations Physics-informed deep learning Hard-encoding of I/BCs
ISSN:	0045-7825, 1879-2138
On-line přístup:	Získat plný text
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