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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

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Abstract

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.

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Funding

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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  1. Patxi Fernandez-Zelaia

    Present address: Oak Ridge National Laboratory, Oak Ridge, TN, 37931, USA

Authors and Affiliations

  1. George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, North Ave, Atlanta, GA, 30332, USA

    Patxi Fernandez-Zelaia, Yuksel C. Yabansu & Surya R. Kalidindi

  2. School of Computational Science and Engineering, Georgia Institute of Technology, North Ave, Atlanta, GA, 30332, USA

    Surya R. Kalidindi

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  1. Patxi Fernandez-Zelaia
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  2. Yuksel C. Yabansu
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Correspondence to Surya R. Kalidindi.

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Fernandez-Zelaia, P., Yabansu, Y.C. & Kalidindi, S.R. 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. Integr Mater Manuf Innov 8, 67–81 (2019). https://doi.org/10.1007/s40192-019-00129-4

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