Readily regenerable reduced microstructure representations

Keita Teranishi, Padma Raghavan, Jingxian Zhang, Tao Wang, Long Qing Chen, Zi Kui Liu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Many of the physical properties of materials are critically dependent on their microstructure. In recent years, there has been increasing interest in using computer simulations based on phase-field models for the spatial and temporal evolution of microstructures. Although such simulations are computationally expensive, the generated set of microstructures can be stored in a repository and used for further analysis in materials design. However, such an approach requires a substantial amount of storage, for example, approximately 1 Terabyte for a single binary alloy. In this paper, we develop fast data compression and regeneration schemes for two-dimensional microstructures that can reduce storage requirements without compromising the accuracy of computed values, such as stress fields used in analysis. Our main contribution is the development and evaluation of a sparse skeletal representation scheme which outperforms traditional compression schemes. Our results indicate that our scheme can reduce microstructure data size by more than two orders of magnitude while achieving better accuracies for the computed stress fields and order parameters.

Original languageEnglish (US)
Pages (from-to)368-379
Number of pages12
JournalComputational Materials Science
Issue number2
StatePublished - Apr 2008

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Chemistry
  • General Materials Science
  • Mechanics of Materials
  • General Physics and Astronomy
  • Computational Mathematics


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