Multiresolution clustering analysis for efficient modeling of hierarchical material systems

Cheng Yu, Orion L. Kafka, Wing Kam Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Direct representation of material microstructure in a macroscale simulation is prohibitively expensive, if even possible, with current methods. However, the information contained in such a representation is highly desirable for tasks such as material/alloy design and manufacturing process control. In this paper, a mechanistic machine learning framework is developed for fast multiscale analysis of material response and structure performance. The new capabilities stem from three major factors: (1) the use of an unsupervised learning (clustering)-based discretization to achieve significant order reduction at both macroscale and microscale; (2) the generation of a database of interaction tensors among discretized material regions; (3) concurrent multiscale response prediction to solve the mechanistic equations. These factors allow for an orders-of-magnitude decrease in the computational expense compared to FEn, n ≥ 2. This method provides sufficiently high fidelity and speed to reasonably conduct inverse modeling for the challenging tasks mentioned above.

Original languageEnglish (US)
Pages (from-to)1293-1306
Number of pages14
JournalComputational Mechanics
Issue number5
StatePublished - May 2021


  • Concurrent multiscale
  • Data-driven
  • Materials design
  • Reduced order modeling
  • Unsupervised learning

ASJC Scopus subject areas

  • Computational Mechanics
  • Ocean Engineering
  • Mechanical Engineering
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Applied Mathematics


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