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


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.

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All authors gratefully acknowledge primary support for this work from AFOSR award FA9550-12-1-0458. Additionally, partial support is also acknowledged from the following grants: NIST award 70NANB14H012; NSF award CCF-1409601; DOE awards DESC0007456, DE-SC0014330; and Northwestern Data Science Initiative.

Author information




RL performed the data experiments and drafted the manuscript with help from YCY, ZY, SRK, and AA. ANC and AA provided the data mining expertise, and SRK provided domain expertise. AA supervised the overall design, development, and implementation of the proposed learning methodology. All authors read and approved the final manuscript.

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Correspondence to Ankit Agrawal.

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Liu, R., Yabansu, Y.C., Yang, Z. et al. Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures. Integr Mater Manuf Innov 6, 160–171 (2017).

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  • Materials informatics
  • Machine learning
  • Elastic localization prediction
  • Ensemble learning
  • Context aware modeling