The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Super-resolution reconstruction of turbulent flows with machine learning
Open accessAbstract
We use machine learning to perform super-resolution analysis of grossly under-resolved turbulent flow field data to reconstruct the high-resolution flow field. Two machine learning models are developed, namely, the convolutional neural network (CNN) and the hybrid downsampled skip-connection/multi-scale (DSC/MS) models. These machine learning models are applied to a two-dimensional cylinder wake as a preliminary test and show remarkable ability to reconstruct laminar flow from low-resolution flow field data. We further assess the performance of these models for two-dimensional homogeneous turbulence. The CNN and DSC/MS models are found to reconstruct turbulent flows from extremely coarse flow field images with remarkable accuracy. For the turbulent flow problem, the machine-leaning-based super-resolution analysis can greatly enhance the spatial resolution with as little as 50 training snapshot data, holding great potential to reveal subgrid-scale physics of complex turbulent flows. With the growing availability of flow field data from high-fidelity simulations and experiments, the present approach motivates the development of effective super-resolution models for a variety of fluid flows.
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Cites
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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
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Self-citationDeep learning methods for super-resolution reconstruction of turbulent flows
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Perspective on machine learning for advancing fluid mechanics
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Shallow neural networks for fluid flow reconstruction with limited sensors
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Metadata
- Authors
- Fukami, K., Fukagata, K., & Taira, K.
- Venue
- Journal of Fluid Mechanics
- Publisher
- Cambridge University Press (CUP)
- Published
- 2019-05-07
- Volume
- 870
- Pages
- 106-120
- ISSN
- 0022-1120 (ISSN-L)
- 1469-7645
- OpenAlex
- W2902480423
- Cited by
- 837
- References
- 22
- FWCI
- 28.02
- Type
- journal article
- Language
- en
- Open access
- green
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