Four datasets for multi-input convolutional network

Published: 21 March 2022| Version 1 | DOI: 10.17632/gg8f2hwkxd.1
Zhuo Wang, Wenhua Yang, Linyan Xiang, Xiao Wang, Yingjie Zhao, Yaohong Xiao, Pengwei Liu, Yucheng Liu, Mihaela Banu, Oleg Zikanov, Lei Chen


Datasets used in paper: "Multi-input convolutional network for ultrafast simulation of field evolvement". Datasets were produced from massive (multi-)physics simulations. They are used to train multi-input convolutional network, which then can act as a cheap substitute of original physics-based models and allows for ultrafast simulation. The datasets and four related physical and engineering problems have distinct characteristics, which should present different challenges to a multi-input ConvNet. They can help comprehensively test the modeling capability of a multi-input ConvNet. Note that the data requires further processing, namely properly preparing multi-input-output pairs, i.e.,((a,X), Y), for training the multi-input convolutional network. Please refer to the paper and code for greater details on how to use the data.


Steps to reproduce

The detailed procedure to produce the datasets can be found in the Experimental Procedures section in the paper.


South Dakota State University, University of Michigan, Jiangsu Normal University, Mississippi State University, University of Michigan Dearborn, Yanshan University


Mechanical Engineering, Fluid Mechanics, Computational Materials Science, Solid Mechanics, Grain Growth, Selective Laser Sintering