An adaptive domain-based POD/ECM hyper-reduced modeling framework without offline training

I. B.C.M. Rocha, F. P. van der Meer*, L. J. Sluys

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

12 Citations (Scopus)
129 Downloads (Pure)


This work presents a reduced-order modeling framework that precludes the need for offline training and adaptively adjusts its lower-order solution space as the analysis progresses. The analysis starts with a fully-solved step and elements are clustered based on their strain response. Elements with the highest strains are solved with a local/global approach in which degrees of freedom from elements undergoing the highest amount of nonlinearity are fully-solved and the rest is approximated by a Proper Orthogonal Decomposition (POD) reduced model with full integration. Elements belonging to the remaining clusters are subjected to a hyper-reduction step using the Empirical Cubature Method (ECM). Online error estimators are used to trigger a retraining process once the reduced solution space becomes inadequate. The performance of the framework is assessed through a series of numerical examples featuring a material model with pressure-dependent plasticity.
Original languageEnglish
Article number112650
Number of pages28
JournalComputer Methods in Applied Mechanics and Engineering
Publication statusPublished - 2020


  • Adaptive reduction
  • Hyper-reduction
  • Local/global approach
  • Reduced-order modeling


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