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

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...

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Bibliographic Details

Published in:	DCC (Los Alamitos, Calif.) pp. 01 - 10
Main Authors:	Momenifar, Mohammadreza, Diao, Enmao, Tarokh, Vahid, Bragg, Andrew D.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.03.2022
Subjects:	Computational fluid dynamics Computational modeling Data compression Deep learning Solid modeling Training data Vector quantization
ISSN:	2375-0359
Online Access:	Get full text
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