Abstract
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
Keywords
Fluid mechanicsFluid dynamicsFlow (mathematics)VisualizationComputer scienceFlow visualizationStokes flowVector fieldNavier–Stokes equationsMotion (physics)Flow velocityArtificial intelligenceMechanicsClassical mechanicsPhysics
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Publication Info
- Year
- 2020
- Type
- article
- Volume
- 367
- Issue
- 6481
- Pages
- 1026-1030
- Citations
- 1697
- Access
- Closed
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Cite This
Maziar Raissi,
Alireza Yazdani,
George Em Karniadakis
(2020).
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations.
Science
, 367
(6481)
, 1026-1030.
https://doi.org/10.1126/science.aaw4741
Identifiers
- DOI
- 10.1126/science.aaw4741
- PMID
- 32001523
- PMCID
- PMC7219083