Journal Article
Science, vol. 367, iss. 6481, pp. 1026-1030, 2020
Authors
Maziar Raissi, Alireza Yazdani, George Em Karniadakis
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
