Tailor-Designed Models for the Turbulent Velocity Gradient through Normalizing Flow Article Swipe
Maurizio Carbone
,
Vincent Joel Peterhans
,
Alexander S. Ecker
,
Michael Wilczek
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1103/physrevlett.133.184001
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1103/physrevlett.133.184001
Small-scale turbulence can be comprehensively described in terms of velocity gradients, which makes them an appealing starting point for low-dimensional modeling. Typical models consist of stochastic equations based on closures for nonlocal pressure and viscous contributions. The fidelity of the resulting models depends on the accuracy of the underlying modeling assumptions. Here, we discuss an alternative data-driven approach leveraging machine learning to derive a velocity gradient model which captures its statistics by construction. We use a normalizing flow to learn the velocity gradient probability density function (PDF) from direct numerical simulation (DNS) of incompressible turbulence. Then, by using the equation for the single-time PDF of the velocity gradient, we construct a deterministic, yet chaotic, dynamical system featuring the learned steady-state PDF by design. Finally, utilizing gauge terms for the velocity gradient single-time statistics, we optimize the time correlations as obtained from our model against the DNS data. As a result, the model time realizations resemble the time series from DNS statistically closely. Published by the American Physical Society 2024
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- en
- Landing Page
- https://doi.org/10.1103/physrevlett.133.184001
- OA Status
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Tailor-Designed Models for the Turbulent Velocity Gradient through Normalizing FlowWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-11-01Full publication date if available
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Maurizio Carbone, Vincent Joel Peterhans, Alexander S. Ecker, Michael WilczekList of authors in order
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https://doi.org/10.1103/physrevlett.133.184001Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1103/physrevlett.133.184001Direct OA link when available
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Turbulence, Statistical physics, Flow (mathematics), Mechanics, Econometrics, Physics, MathematicsTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2024-11-01 |
| publication_year | 2024 |
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