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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Bibliographic Details
Published in:	Computer methods in applied mechanics and engineering Vol. 389; p. 114399
Main Authors:	Ren, Pu, Rao, Chengping, Liu, Yang, Wang, Jian-Xun, Sun, Hao
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.02.2022 Elsevier BV
Subjects:	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
Online Access:	Get full text
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