There is a newer version of the record available.

Published August 31, 2024 | Version v1

Dataset for publication: Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform

Description

This dataset provides the necessary data to get the images and results shown in the paper "Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform". 

Files with extension .dream3d are accompained by a file with extension .xdmf. This files can be opened with Paraview. And their data can be accesible using python or matlab.

For more information contact Proffesor Somnath Ghosh at Johns Hopkins University, Civil and Systems Engineering Department.

Files

AA7050.zip

Files (176.3 MB)

Name Size Download all
md5:fcbe5f821debd9ad77446638e3d4b0ce
109.1 MB Preview Download
md5:9ee37c1d022ed5a2254caab82680b34b
950 Bytes Preview Download
md5:0e8a6eb729546cec2b276c5e88449893
5.5 MB Preview Download
md5:7c5a8fdad60c8322862de607a4385d26
61.7 MB Preview Download

Additional details

Funding

Office of Naval Research
Aero Structures & Materials program N00014-20-1-4004
U.S. National Science Foundation
Advanced Research Computing at Hopkins (ARCH) c OAC 1920103
United States Air Force Office of Scientific Research
AFOSR DURIP FA9550-21-1-0303

Software

Repository URL
https://doi.org/10.5281/zenodo.13372514
Programming language
Python , Fortran , MATLAB
Development Status
Active

References

  • Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform