Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

Journal Article · Fri Feb 28 00:00:00 EST 2020 · Science

DOI:https://doi.org/10.1126/science.aaw4741· OSTI ID:1631426

Raissi, Maziar ^[1]; Yazdani, Alireza ^[2]; Karniadakis, George E. ^[3]

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1. University of Colorado at Boulder
2. Brown University
3. BROWN UNIVERSITY

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.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1631426
• Report Number(s): PNNL-SA-152906
• Journal Information: Science, Vol. 367, Issue 6481
• Country of Publication: United States
• Language: English