Data-driven reduced order models for effective yield strength and partitioning of strain in multiphase materials

Marat I. Latypov, Surya R. Kalidindi

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


There is a critical need for the development and verification of practically useful multiscale modeling strategies for simulating the mechanical response of multiphase metallic materials with heterogeneous microstructures. In this contribution, we present data-driven reduced order models for effective yield strength and strain partitioning in such microstructures. These models are built employing the recently developed framework of Materials Knowledge Systems that employ 2-point spatial correlations (or 2-point statistics) for the quantification of the heterostructures and principal component analyses for their low-dimensional representation. The models are calibrated to a large collection of finite element (FE) results obtained for a diverse range of microstructures with various sizes, shapes, and volume fractions of the phases. The performance of the models is evaluated by comparing the predictions of yield strength and strain partitioning in two-phase materials with the corresponding predictions from a classical self-consistent model as well as results of full-field FE simulations. The reduced-order models developed in this work show an excellent combination of accuracy and computational efficiency, and therefore present an important advance towards computationally efficient microstructure-sensitive multiscale modeling frameworks.

Original languageEnglish (US)
Pages (from-to)242-261
Number of pages20
JournalJournal of Computational Physics
StatePublished - Oct 1 2017
Externally publishedYes


  • 2-point statistics
  • Data science
  • Homogenization theories
  • Multiphase materials
  • Reduced-order models
  • Strain partitioning

ASJC Scopus subject areas

  • Numerical Analysis
  • Modeling and Simulation
  • Physics and Astronomy (miscellaneous)
  • Physics and Astronomy(all)
  • Computer Science Applications
  • Computational Mathematics
  • Applied Mathematics


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