Machine learning approaches for elastic localization linkages in high-contrast composite materials
Abstract
There has been a growing recognition of the opportunities afforded by advanced data science and informatics approaches in addressing the computational demands of modeling and simulation of multiscale materials science phenomena. More specifically, the mining of microstructure–property relationships by various methods in machine learning and data mining opens exciting new opportunities that can potentially result in a fast and efficient material design. This work explores and presents multiple viable approaches for computationally efficient predictions of the microscale elastic strain fields in a three-dimensional (3-D) voxel-based microstructure volume element (MVE). Advanced concepts in machine learning and data mining, including feature extraction, feature ranking and selection, and regression modeling, are explored as data experiments. Improvements are demonstrated in a gradually escalated fashion achieved by (1) feature descriptors introduced to represent voxel neighborhood characteristics, (2) a reduced set of descriptors with top importance, and (3) an ensemble-based regression technique.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 01, 2015
- Source ID
- 10.1186/s40192-015-0042-z
Entities
People
- Alok N. Choudhary
- Ankit Agrawal
- Ruoqian Liu
- Surya R. Kalidindi
- Yuksel C Yabansu
Organizations
- Air Force Office of Scientific Research
- National Institute of Standards and Technology
- National Science Foundation
- United States Department of Energy