The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
Open accessAbstract
Machine-learning fluid flow: Quantifying fluid flow is relevant to disciplines ranging from geophysics to medicine. Flow can be experimentally visualized using, for example, smoke or contrast agents, but extracting velocity and pressure fields from this information is tricky. Raissi et al. developed a machine-learning approach to tackle this problem. Their method exploits the knowledge of Navier-Stokes equations, which govern the dynamics of fluid flow in many scientifically relevant situations. The authors illustrate their approach using examples such as blood flow in an aneurysm. Science , this issue p. 1026
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Cites
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Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.10.045 · Cited by 20,384
Self-citationApproximation by superpositions of a sigmoidal function
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Cited by
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
Self-citationInterpretable machine learning: Fundamental principles and 10 grand challenges
Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022) · Statistics Surveys · doi:10.1214/21-ss133 · Cited by 958
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
Yu, J., Lu, L., Meng, X., & Karniadakis, G. E. (2022) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2022.114823 · Cited by 722
Self-citationEnforcing Analytic Constraints in Neural Networks Emulating Physical Systems
Beucler, T., Pritchard, M., Rasp, S., Ott, J., Baldi, P., & Gentine, P. (2021) · Physical Review Letters · doi:10.1103/physrevlett.126.098302 · Cited by 451
A review of Earth Artificial Intelligence
Sun, Z., Sandoval, L., Crystal-Ornelas, R., Mousavi, S. M., Wang, J., Lin, C., Cristea, N., Tong, D., Carande, W. H., Ma, X., et al. (2022) · Computers & Geosciences · doi:10.1016/j.cageo.2022.105034 · Cited by 271
Metadata
- Authors
- Raissi, M., Yazdani, A., & Karniadakis, G. E.
- Venue
- Science
- Published
- 2020-02-28
- Volume
- 367
- Issue
- 6481
- Pages
- 1026-1030
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W3003922491
- Cited by
- 2,103
- References
- 20
- FWCI
- 92.03
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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