The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Deep learning in fluid dynamics
Open accessAbstract
It was only a matter of time before deep neural networks (DNNs) – deep learning – made their mark in turbulence modelling, or more broadly, in the general area of high-dimensional, complex dynamical systems. In the last decade, DNNs have become a dominant data mining tool for big data applications. Although neural networks have been applied previously to complex fluid flows, the article featured here (Ling et al., J. Fluid Mech., vol. 807, 2016, pp. 155–166) is the first to apply a true DNN architecture, specifically to Reynolds averaged Navier Stokes turbulence models. As one often expects with modern DNNs, performance gains are achieved over competing state-of-the-art methods, suggesting that DNNs may play a critically enabling role in the future of modelling complex flows.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680
Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
Top 10 algorithms in data mining
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., et al. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-007-0114-2 · Cited by 5,667
Cited by
Super-resolution reconstruction of turbulent flows with machine learning
Fukami, K., Fukagata, K., & Taira, K. (2019) · Journal of Fluid Mechanics · doi:10.1017/jfm.2019.238 · Cited by 837
Zhu, Y., & Zabaras, N. (2018) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.04.018 · Cited by 704
Physics-informed neural networks for solving Reynolds-averaged Navier–Stokes equations
Eivazi, H., Tahani, M., Schlatter, P., & Vinuesa, R. (2022) · Physics of Fluids · doi:10.1063/5.0095270 · Cited by 490
Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
Murata, T., Fukami, K., & Fukagata, K. (2020) · Journal of Fluid Mechanics · doi:10.1017/jfm.2019.822 · Cited by 359
Deep learning methods for super-resolution reconstruction of turbulent flows
Liu, B., Tang, J., Huang, H., & Lu, X.-Y. (2020) · Physics of Fluids · doi:10.1063/1.5140772 · Cited by 359
Metadata
- Authors
- Kutz, J. N.
- Venue
- Journal of Fluid Mechanics
- Publisher
- Cambridge University Press (CUP)
- Published
- 2017-01-31
- Volume
- 814
- Pages
- 1-4
- ISSN
- 0022-1120 (ISSN-L)
- 1469-7645
- OpenAlex
- W2585298970
- Cited by
- 831
- References
- 5
- FWCI
- 27.23
- Type
- journal article
- Language
- en
- Open access
- bronze
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