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 G32: Machine Learning and Data Driven Models II
10:35 AM–12:45 PM,
Monday, November 19, 2018
Georgia World Congress Center
Room: B404
Chair: Alireza Yazdani, Brown University
Abstract ID: BAPS.2018.DFD.G32.5
Abstract: G32.00005 : Data-driven prediction of unsteady flow over a circular cylinder using deep learning*
11:27 AM–11:40 AM
Presenter:
Sangseung Lee
(Pohang University of Science and Technology)
Authors:
Sangseung Lee
(Pohang University of Science and Technology)
Donghyun You
(Pohang University of Science and Technology)
Unsteady flow fields over a circular cylinder are predicted using deep learning networks. Deep learning networks construct nonlinear mappings that allows prediction of flow fields at future occasions based on flow fields at past occasions. Deep learning networks equipped with four different loss-function configurations are trained using flow fields at ReD = 100, 200, 300, and 400. Two networks are trained using different loss-function sets: with and without loss functions for mass and momentum conservation, and two other networks are trained using loss-function sets: with and without loss functions for mass and momentum conservation both with a loss function for adversarial training. The trained networks are employed to predict flow fields at ReD = 500, and 3000, at which Reynolds numbers, the networks are not exposed to flow fields a priori. Results predicted by each network are compared and analyzed to identify effects of the configuration of loss functions and the use of adversarial training on the predictive performance.
*This work was supported by Samsung Research Funding Center of Samsung Electronics under Project Number SRFC-TB1703-01 and National Research Foundation of Korea (NRF) under Project Number NRF-2017R1E1A1A03070514.
To cite this abstract, use the following reference: http://meetings.aps.org/link/BAPS.2018.DFD.G32.5
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