V2V: A Deep Learning Approach to Variable-to-Variable Selection and Translation for Multivariate Time-Varying Data

Pytorch implementation for V2V: A Deep Learning Approach to Variable-to-Variable Selection and Translation for Multivariate Time-Varying Data.

Prerequisites

• Linux
• CUDA >= 10.0
• Python >= 3.7
• Numpy
• Pytorch >= 1.0

Data format

The volume at each time step is saved as a .dat file with the little-endian format. The data is stored in column-major order, that is, z-axis goes first, then y-axis, finally x-axis.

Training models

cd Code 

• training

python3 main.py --mode 'train' --dataset 'Combustion'

• inference

python3 main.py --mode 'inf'

Citation

@article{Han-VIS20,
	Author = {J. Han and H. Zheng and Y. Xing and D. Z. Chen and C. Wang},
	Journal = {IEEE Transactions on Visualization and Computer Graphics},
	Number = {2},
	Pages = {1290-1300},
	Title = {{V2V}: A Deep Learning Approach to Variable-to-Variable Selection and Translation for Multivariate Time-Varying Data},
	Volume = {27},
	Year = {2021}}

Acknowledgements

This research was supported in part by the U.S. National Science Foundation through grants IIS-1455886, CCF-1617735, CNS-1629914, DUE-1833129, and IIS-1955395.