Bulletin of the American Physical Society
71st Annual Meeting of the APS Division of Fluid Dynamics
Volume 63, Number 13
Sunday–Tuesday, November 18–20, 2018; Atlanta, Georgia
Session Q29: Turbulent Mixing II
12:50 PM–3:13 PM,
Tuesday, November 20, 2018
Georgia World Congress Center
Room: B401
Chair: Emmanuel Villermaux, Aix-Marseille University
Abstract ID: BAPS.2018.DFD.Q29.3
Abstract: Q29.00003 : Deep Learning of PDF Turbulence Closure
1:16 PM–1:29 PM
Presenter:
Maziar Raissi
(Brown University)
Authors:
Maziar Raissi
(Brown University)
Hessam Babaee
(Univ of Pittsburgh)
Peyman Givi
(Univ of Pittsburgh)
A new data-driven method is presented for learning the PDF turbulence closure using deep learning. The method is based on a recently developed physics-informed deep learning model and relies on the physics as expressed by partial differential equations. We solve the single-point PDF equation in homogeneous turbulence using deep neural networks to describe the classical binary scalar mixing problem. In this setting, the neural network learns the conditional expected statistics via observing the PDF data. The performance of this data-driven strategy is appraised against the exact solution where the PDF is given by the amplitude mapping closure (AMC) of Kriachnan.
To cite this abstract, use the following reference: http://meetings.aps.org/link/BAPS.2018.DFD.Q29.3
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