The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Perspective on machine learning for advancing fluid mechanics
Open accessAbstract
A perspective is presented on how machine learning methods might advance fluid mechanics. Current limitations are discussed, though the potential impact is deemed high, so long as outcomes are held to the long-standing critical standards that should guide studies of flow physics.
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Cites
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Machine Learning for Fluid Mechanics
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“The progress in machine learning that led to these advances has been mostly technological, and is well reviewed for fluid mechanics elsewhere [1].”
BackgroundPrediction of cardiovascular risk factors from retinal fundus photographs via deep learning
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Turbulence Modeling in the Age of Data
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Super-resolution reconstruction of turbulent flows with machine learning
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Model Reduction for Flow Analysis and Control
Rowley, C. W., & Dawson, S. T. M. (2017) · Annual Review of Fluid Mechanics · doi:10.1146/annurev-fluid-010816-060042 · Cited by 792
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Deep learning of vortex-induced vibrationsPreprint
Raissi, M., Wang, Z., Triantafyllou, M. S., & Karniadakis, G. E. (2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 361
Data-assisted reduced-order modeling of extreme events in complex dynamical systems
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Machine-Learning-Based Detection of Aerodynamic Disturbances Using Surface Pressure Measurements
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Self-citationMachine learning the kinematics of spherical particles in fluid flows
Wan, Z. Y., & Sapsis, T. P. (2018) · Journal of Fluid Mechanics · doi:10.1017/jfm.2018.797 · Cited by 49
Machine learning-assisted early ignition prediction in a complex flow
Popov, P. P., Buchta, D., Anderson, M. J., Massa, L. L., Capecelatro, J., Bodony, D. J., & Freund, J. B. (2019) · Combustion and Flame · doi:10.1016/j.combustflame.2019.05.014 · Cited by 29
Self-citationEfficient collective swimming by harnessing vortices through deep reinforcement learning
Verma, S., Novati, G., & Koumoutsakos, P. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1800923115 · Cited by 10
Wang, J.-X., Wu, J.-L., & Xiao, H. (2016) · arXiv · doi:10.48550/arxiv.1606.07987 · Cited by 1
Metadata
- Authors
- Brenner, M. P., Eldredge, J. D., & Freund, J. B.
- Venue
- Physical Review Fluids
- Publisher
- American Physical Society (APS)
- Published
- 2019-10-16
- Volume
- 4
- Issue
- 10
- ISSN
- 2469-990X (ISSN-L)
- OpenAlex
- W2981246174
- Cited by
- 292
- References
- 23
- FWCI
- 16.53
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
