Dimension Reduced Turbulent Flow Data From Deep Vector Quantizers

Last update: Sep 12, 2022

Overview

Dimension Reduced Turbulent Flow Data From Deep Vector Quantizers

This is an implementation of A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow
This is an implementation of Dimension Reduced Turbulent Flow Data From Deep Vector Quantizers

Requirements

see requirements.txt

Instruction

Global hyperparameters are configured in config.yml
Hyperparameters can be found at process_control() in utils.py

Examples

Train vqvae, compression scale 1, regularization parameter $\alpha=0.1,\gamma=0$

python train_vqvae.py --data_name Turb --model_name vqvae --control_name 1_exact-physcis_0.1-0

Test vqvae, compression scale 3, regularization parameter $\alpha=0.1,\gamma=0.0001$

python test_vqvae.py --data_name Turb --model_name vqvae --control_name 3_exact-physcis_0.1-0.0001

Results

Schematic of the VQ-AE architecture.

Comparing original and reconstructed 3D (a) stationary isotropic, (b) decaying isotropic, and (c) Taylor-Green vortex turbulence compressed by VQ-AE.

(a) with and (b) without regularizations for PDFs of normalized longitudinal (left), transverse (middle) components of velocity gradient tensor, and Turbulence Kinetic Energy spectra (right) of stationary isotropic turbulence flow.

Acknowledgement

Mohammadreza Momenifar
Enmao Diao
Vahid Tarokh
Andrew D. Bragg

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Dimension Reduced Turbulent Flow Data From Deep Vector Quantizers

Related tags

Overview

Dimension Reduced Turbulent Flow Data From Deep Vector Quantizers

Requirements

Instruction

Examples

Results

Acknowledgement

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Owner

DreamSoul

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