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A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow

Publication ,  Conference
Momenifar, M; Diao, E; Tarokh, V; Bragg, AD
Published in: Data Compression Conference Proceedings
January 1, 2022

Analyzing large-scale data from simulations of turbulent flows is memory intensive, requiring significant resources. This major challenge highlights the need for data compression techniques. In this study, we apply a physics-informed Deep Learning technique based on vector quantization to generate a discrete, low-dimensional representation of data from simulations of three-dimensional turbulent flows. The deep learning framework is composed of convolutional layers and incorporates physical constraints on the flow, such as preserving incompressibility and global statistical characteristics of the velocity gradients. The accuracy of the model is assessed using statistical, comparison-based similarity and physics-based metrics. The training data set is produced from Direct Numerical Simulation of an incompressible, statistically stationary, isotropic turbulent flow. The performance of this lossy data compression scheme is evaluated not only with unseen data from the stationary, isotropic turbulent flow, but also with data from decaying isotropic turbulence, and a Taylor-Green vortex flow. Defining the compression ratio (CR) as the ratio of original data size to the compressed one, the results show that our model based on vector quantization can offer CR = 85 with a mean square error (MSE) of O(10-3), and predictions that faithfully reproduce the statistics of the flow, except at the very smallest scales where there is some loss. Compared to the recent study based on a conventional autoencoder where compression is performed in a continuous space, our model improves the CR by more than 30 percent, and reduces the MSE by an order of magnitude. Our compression model is an attractive solution for situations where fast, high quality and low-overhead encoding and decoding of large data are required.

Duke Scholars

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Published In

Data Compression Conference Proceedings

DOI

ISSN

1068-0314

Publication Date

January 1, 2022

Volume

2022-March

Start / End Page

312 / 321
 

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Momenifar, M., Diao, E., Tarokh, V., & Bragg, A. D. (2022). A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow. In Data Compression Conference Proceedings (Vol. 2022-March, pp. 312–321). https://doi.org/10.1109/DCC52660.2022.00039
Momenifar, M., E. Diao, V. Tarokh, and A. D. Bragg. “A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow.” In Data Compression Conference Proceedings, 2022-March:312–21, 2022. https://doi.org/10.1109/DCC52660.2022.00039.
Momenifar M, Diao E, Tarokh V, Bragg AD. A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow. In: Data Compression Conference Proceedings. 2022. p. 312–21.
Momenifar, M., et al. “A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow.” Data Compression Conference Proceedings, vol. 2022-March, 2022, pp. 312–21. Scopus, doi:10.1109/DCC52660.2022.00039.
Momenifar M, Diao E, Tarokh V, Bragg AD. A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow. Data Compression Conference Proceedings. 2022. p. 312–321.

Published In

Data Compression Conference Proceedings

DOI

ISSN

1068-0314

Publication Date

January 1, 2022

Volume

2022-March

Start / End Page

312 / 321