Dissertation Proposal - Amir Abbas Kazemzadeh
December 2 @ 3:00 pm - 4:30 pm MST
Deep Learning Approach for Chemistry and Processing History Prediction from Materials Microstructure
Amir Abbas Kazemzadeh – Data Science
Finding the chemical composition and processing history from a microstructure morphology for heterogeneous materials is desired in many applications. While the simulation methods based on physical concepts such as the phase-field method can predict the spatio-temporal evolution of the materials’ microstructure, they are not efficient techniques for predicting processing and chemistry if a specific morphology is desired. In this study, we propose a framework based on a deep learning approach that enables us to predict the chemistry and processing history just by reading the morphological distribution of one element.