A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality

M. A. Bessa, R. Bostanabad, Z. Liu, A. Hu, Daniel W. Apley, C. Brinson, W. Chen, Wing Kam Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

164 Scopus citations

Abstract

A new data-driven computational framework is developed to assist in the design and modeling of new material systems and structures. The proposed framework integrates three general steps: (1) design of experiments, where the input variables describing material geometry (microstructure), phase properties and external conditions are sampled; (2) efficient computational analyses of each design sample, leading to the creation of a material response database; and (3) machine learning applied to this database to obtain a new design or response model. In addition, the authors address the longstanding challenge of developing a data-driven approach applicable to problems that involve unacceptable computational expense when solved by standard analysis methods – e.g. finite element analysis of representative volume elements involving plasticity and damage. In these cases the framework includes the recently developed “self-consistent clustering analysis” method in order to build large databases suitable for machine learning. The authors believe that this will open new avenues to finding innovative materials with new capabilities in an era of high-throughput computing (“big-data”).

Original languageEnglish (US)
Pages (from-to)633-667
Number of pages35
JournalComputer Methods in Applied Mechanics and Engineering
Volume320
DOIs
StatePublished - Jun 15 2017

Keywords

  • Design of experiments
  • Machine learning and data mining
  • Plasticity
  • Reduced order model
  • Self-consistent clustering analysis

ASJC Scopus subject areas

  • Computational Mechanics
  • Mechanics of Materials
  • Mechanical Engineering
  • Physics and Astronomy(all)
  • Computer Science Applications

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