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

Ruoqian Liu, Yuksel C Yabansu, Zijiang Yang, Alok Nidhi Choudhary, Surya R. Kalidindi, Ankit Agrawal

Research output: Contribution to journalArticle

26 Scopus citations

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.
Original languageEnglish (US)
Pages (from-to)160–171
Number of pages12
JournalIntegrating Materials and Manufacturing Innovation
Volume6
Issue number2
DOIs
StatePublished - 2017

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