Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 28, 2020
- Source ID
- 10.1126/science.aaw4741
Entities
People
- Alireza Yazdani
- George Karniadakis
- Maziar Raissi
Organizations
- Air Force Office of Scientific Research
- Brown University
- National Institutes of Health
- Nvidia
- United States Department of Energy