A Comparative Study of the Efficacy of Local/Global and Parametric/Nonparametric Machine Learning Methods for Establishing Structure–Property Linkages in High-Contrast 3D Elastic Composites

  • Patxi Fernandez-Zelaia
  • Yuksel C. Yabansu
  • Surya R. KalidindiEmail author
Technical Article


Reduced-order structure–property (S-P) linkages play a pivotal role in the tailored design of materials for advanced engineering components. There is a critical need to distill these from the simulation datasets aggregated using sophisticated, computationally expensive, physics-based numerical tools (e.g., finite element methods). The recent emergence of materials data science approaches has opened new avenues for addressing this challenge. In this paper, we critically compare the relative merits of the application of four distinct machine learning approaches for their efficacy in extracting microstructure-property linkages from the finite element simulation data aggregated on high-contrast elastic composites with different microstructures. The machine learning approaches selected for the study have included different combinations of local/global and parametric/nonparametric approaches. Furthermore, the nonparametric approaches selected for this study are based on Gaussian Process (GP) models that allow for a formal treatment of uncertainty quantification in the predicted values. The predictive performances of these different approaches have been compared against each other using rigorous cross-validation error metrics. Furthermore, their sensitivity to both the dataset size and dimensionality has been investigated.


Nonparametric regression Local approximate Gaussian Process Structure–property linkages High-contrast composites Gaussian Process regression 


Funding Information

YCY and SRK received support from NSF 1761406. PFZ received financial support of the work from the Morris M. Bryan, Jr. Professorship.


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Copyright information

© The Minerals, Metals & Materials Society 2019

Authors and Affiliations

  1. 1.George W. Woodruff School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Oak Ridge National LaboratoryOak RidgeUSA
  3. 3.School of Computational Science and EngineeringGeorgia Institute of TechnologyAtlantaUSA

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