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

  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

  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