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

Raissi, Maziar; Yazdani, Alireza; Karniadakis, George Em

doi:10.1126/science.aaw4741

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

Journal Article · Wed Jan 29 23:00:00 EST 2020 · Science

DOI:https://doi.org/10.1126/science.aaw4741· OSTI ID:1601918

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Division of Applied Mathematics, Brown University, Providence, RI 02906, USA., NVIDIA Corporation, Santa Clara, CA 95051, USA.
Division of Applied Mathematics, Brown University, Providence, RI 02906, USA.

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.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 1601918

Alternate ID(s):: OSTI ID: 1631426
OSTI ID: 2281994

Journal Information:: Science, Journal Name: Science Journal Issue: 6481 Vol. 367; ISSN 0036-8075

Publisher:: American Association for the Advancement of Science (AAAS)Copyright Statement

Country of Publication:: United States

Language:: English

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