Generating Input Data for Microstructure Modelling: A Deep Learning Approach Using Generative Adversarial Networks

Generating Input Data for Microstructure Modelling: A Deep Learning Approach Using Generative Adversarial Networks

For the generation of representative volume elements a statistical description of the relevant parameters is necessary. These parameters usually describe the geometric structure of a single grain. Commonly, parameters like area, aspect ratio, and slope of the grain axis relative to the rolling direc...

Full description

Bibliographic Details
Main Authors: Felix Pütz, Manuel Henrich, Niklas Fehlemann, Andreas Roth, Sebastian Münstermann
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Materials
Subjects:
microstructure modelling
representative volume elements
dp-steel
machine learning
deep learning
wasserstein gan
Online Access:https://www.mdpi.com/1996-1944/13/19/4236

Internet

https://www.mdpi.com/1996-1944/13/19/4236

Similar Items