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Learning Wave Propagation with Attention-Based Convolutional Recurrent Autoencoder Net
[article]
2022
arXiv
pre-print
In this paper, we present an end-to-end attention-based convolutional recurrent autoencoder network (AB-CRAN) for data-driven modeling of wave propagation phenomena. To construct the low-dimensional learning model, we employ a denoising-based convolutional autoencoder from the full-order snapshots of wave propagation generated by solving hyperbolic partial differential equations. The proposed deep neural network architecture relies on the attention-based recurrent neural network with long
arXiv:2201.06628v3
fatcat:gr74fjdqrfezfdhm5bt4ajlhjy