Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 7-Day Trial for You or Your Team.

Learn More →

Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures

Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional... The response of a composite material is the result of a complex interplay between the prevailing mechanics and the heterogenous structure at disparate spatial and temporal scales. Understanding and capturing the multiscale phenomena is critical for materials modeling and can be pursued both by physical simulation-based modeling as well as data-driven machine learning-based modeling. In this work, we build machine learning-based data models as surrogate models for approximating the microscale elastic response as a function of the material microstructure (also called the elastic localization linkage). In building these surrogate models, we particularly focus on understanding the role of contexts, as a link to the higher scale information that most evidently influences and determines the microscale response. As a result of context modeling, we find that machine learning systems with context awareness not only outperform previous best results, but also extend the parallelism of model training so as to maximize the computational efficiency. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Integrating Materials and Manufacturing Innovation Springer Journals

Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures

Download PDF
12 pages

 
/lp/springer-journal/context-aware-machine-learning-approaches-for-modeling-elastic-haDSXeT3n0

References (68)

Publisher
Springer Journals
Copyright
Copyright © 2017 by The Minerals, Metals & Materials Society
Subject
Materials Science; Metallic Materials; Characterization and Evaluation of Materials; Structural Materials; Surfaces and Interfaces, Thin Films; Nanotechnology
ISSN
2193-9764
eISSN
2193-9772
DOI
10.1007/s40192-017-0094-3
Publisher site
See Article on Publisher Site

Abstract

The response of a composite material is the result of a complex interplay between the prevailing mechanics and the heterogenous structure at disparate spatial and temporal scales. Understanding and capturing the multiscale phenomena is critical for materials modeling and can be pursued both by physical simulation-based modeling as well as data-driven machine learning-based modeling. In this work, we build machine learning-based data models as surrogate models for approximating the microscale elastic response as a function of the material microstructure (also called the elastic localization linkage). In building these surrogate models, we particularly focus on understanding the role of contexts, as a link to the higher scale information that most evidently influences and determines the microscale response. As a result of context modeling, we find that machine learning systems with context awareness not only outperform previous best results, but also extend the parallelism of model training so as to maximize the computational efficiency.

Journal

Integrating Materials and Manufacturing Innovation – Springer Journals

Published: Mar 31, 2017

There are no references for this article.