Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
For centuries, flow visualization has been the art of making fluid motion visible in physical and biological systems. Although such flow patterns can be, in principle, described by the Navier-Stokes equations, extracting the velocity and pressure fields directly from the images is challenging. We addressed this problem by developing hidden fluid mechanics (HFM), a physics-informed deep-learning framework capable of encoding the Navier-Stokes equations into the neural networks while being agnostic to the geometry or the initial and boundary conditions. We demonstrate HFM for several physical and biomedical problems by extracting quantitative information for which direct measurements may not be possible. HFM is robust to low resolution and substantial noise in the observation data, which is important for potential applications.
- Research Organization:
- Brown Univ., Providence, RI (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- Grant/Contract Number:
- AC05-76RL01830; SC0019453
- OSTI ID:
- 1601918
- Alternate ID(s):
- OSTI ID: 2281994
- Journal Information:
- Science, Journal Name: Science Vol. 367 Journal Issue: 6481; ISSN 0036-8075
- Publisher:
- American Association for the Advancement of Science (AAAS)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Similar Records
NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks