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.G32.8
Abstract: G32.00008 : Physics-Informed Generative Adversarial Networks by Incorporating Conservation Laws
12:06 PM–12:19 PM
Presenter:
Yang Zeng
(Virginia Tech)
Authors:
Yang Zeng
(Virginia Tech)
Jinlong Wu
(Virginia Tech)
Heng Xiao
(Virginia Tech)
Recently, machine learning techniques have proven to be successful in many data-driven physical modeling tasks, including in mimicking distributions of processes in complex systems using a flavor of deep neural networks called generative adversarial networks (GANs). GANs have also been designed to generate solutions of PDEs governing complex systems without having to numerically solve these PDEs, by using existing high-fidelity simulations or experimental data as training data. In this work, we present a physics-informed GAN by enforcing constraints of conservation laws to improve the quality of the generated solutions of GANs. We show that this physics-informed GAN generates more realistic solutions of potential flows compared to traditional GANs without any physical constraints. These results suggest that the physics-informed GAN is more suitable for the task of physical modeling and has great potential in many areas where directly simulating the physics is usually expansive, e.g., turbulence.
To cite this abstract, use the following reference: http://meetings.aps.org/link/BAPS.2018.DFD.G32.8
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