The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Enhancing computational fluid dynamics with machine learning
Open accessAbstract
Machine learning is rapidly becoming a core technology for scientific computing, with numerous opportunities to advance the field of computational fluid dynamics. Here we highlight some of the areas of highest potential impact, including to accelerate direct numerical simulations, to improve turbulence closure modeling and to develop enhanced reduced-order models. We also discuss emerging areas of machine learning that are promising for computational fluid dynamics, as well as some potential limitations that should be taken into account. Machine learning has been used to accelerate the simulation of fluid dynamics. However, despite the recent developments in this field, there are still challenges to be addressed by the community, a fact that creates research opportunities.
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Cites
Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.10.045 · Cited by 20,384
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
Physics-informed machine learning
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00314-5 · Cited by 8,718
Turbulence statistics in fully developed channel flow at low Reynolds number
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Machine Learning for Fluid Mechanics
Brunton, S. L., Noack, B. R., & Koumoutsakos, P. (2020) · Annual Review of Fluid Mechanics · doi:10.1146/annurev-fluid-010719-060214 · Cited by 3,063 · cited 1×
“Several authors have surveyed the potential of machine learning to improve fluid mechanics [11, 12], including topics beyond the scope of CFD, such as experimental techniques, control applications, and related fields.”
BackgroundSelf-citationThe quiet revolution of numerical weather prediction
Bauer, P., Thorpe, A., & Brunet, G. (2015) · Nature · doi:10.1038/nature14956 · Cited by 2,729
Wang, J.-X., Wu, J.-L., & Xiao, H. (2017) · Physical Review Fluids · doi:10.1103/physrevfluids.2.034603 · Cited by 737
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Metadata
- Authors
- Vinuesa, R., & Brunton, S. L.
- Venue
- Nature Computational Science
- Published
- 2022-06-27
- Volume
- 2
- Issue
- 6
- Pages
- 358-366
- ISSN
- 2662-8457 (ISSN-L)
- OpenAlex
- W4286909924
- Cited by
- 596
- References
- 8
- FWCI
- 41.28
- Type
- journal article
- Language
- en
- Open access
- green
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