AMGNET: multi-scale graph neural networks for flow field prediction-Reference-Cited by-同舟云学术

AMGNET: multi-scale graph neural networks for flow field prediction

Published:2022-10-13 Issue:1 Volume:34 Page:2500-2519
ISSN:0954-0091
Container-title:Connection Science
language:en
Short-container-title:Connection Science

Author:

Yang Zhishuang¹,Dong Yidao²,Deng Xiaogang³,Zhang Laiping⁴

Affiliation:

1. College of Computer Science, Sichuan University, Sichuan, People's Republic of China

2. College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, People's Republic of China

3. Academy of Military Sciences, Beijing, People's Republic of China

4. Unmanned Systems Research Center, National Innovation Institute of Defense Technology, Beijing, People's Republic of China

Funder

National Key Project of China

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Human-Computer Interaction,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/09540091.2022.2131737

Reference37 articles.

1. Prediction of aerodynamic flow fields using convolutional neural networks

2. Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M. & Faulkner, R. (2018). Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261

3. A New Convergence Proof for the Multigrid Method Including the V-Cycle

4. Brandt, A., McCormick, S. F. & Ruge, J. (1984). Algebraic multigrid (AMG) for sparse matrix equations. In D. J. Evans (Ed.), Sparsity and its applications (pp. 257–284). Cambridge University Press.

5. Self-adaptive spatial-temporal network based on heterogeneous data for air quality prediction

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