Assessment of supervised machine learning methods for fluid flows-Reference-Cited by-同舟云学术

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Assessment of supervised machine learning methods for fluid flows

Published:2020-02-27 Issue:4 Volume:34 Page:497-519
ISSN:0935-4964
Container-title:Theoretical and Computational Fluid Dynamics
language:en
Short-container-title:Theor. Comput. Fluid Dyn.

Author:

Fukami Kai,Fukagata Koji,Taira Kunihiko

Funder

Japan Society for the Promotion of Science

US Army Research Office

Air Force Office of Scientific Research

Publisher

Springer Science and Business Media LLC

Subject

Fluid Flow and Transfer Processes,General Engineering,Condensed Matter Physics,Computational Mechanics

Link

http://link.springer.com/content/pdf/10.1007/s00162-020-00518-y.pdf

Reference64 articles.

1. Kutz, J.N.: Deep learning in fluid dynamics. J. Fluid Mech. 814, 1–4 (2017)

2. Brunton, S.L., Noack, B.R., Koumoutsakos, P.: Machine learning for fluid mechanics. Annu. Rev. Fluid Mech. 52, 477–508 (2020)

3. Brenner, M.P., Eldredge, J.D., Freund, J.B.: Perspective on machine learning for advancing fluid mechanics. Phys. Rev. Fluids 4, 100501 (2019)

4. Kreinovich, V.Y.: Arbitrary nonlinearity is sufficient to represent all functions by neural networks: a theorem. Neural Netw. 4, 381–383 (1991)

5. Hornik, K.: Approximation capabilities of multilayer feedforward networks. Neural Netw. 4, 251–257 (1991)

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