A bayesian inference-based approach to empirical training of strongly coupled constituent models

G. S. Flynn, E. Chodora, S. Atamturktur, D. A. Brown

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Partitioned analysis enables numerical representation of complex systems through the coupling of smaller, simpler constituent models, each representing a different phenomenon, domain, scale, or functional component. Through this coupling, inputs and outputs of constituent models are exchanged in an iterative manner until a converged solution satisfies all constituents. In practical applications, numerical models may not be available for all constituents due to lack of understanding of the behavior of a constituent and the inability to conduct separate-effect experiments to investigate the behavior of the constituent in an isolated manner. In such cases, empirical representations of missing constituents have the opportunity to be inferred using integral-effect experiments, which capture the behavior of the system as a whole. Herein, we propose a Bayesian inferencebased approach to estimate missing constituent models from available integral-effect experiments. Significance of this novel approach is demonstrated through the inference of a material plasticity constituent integrated with a finite element model to enable efficient multiscale elasto-plastic simulations.

Original languageEnglish (US)
Article number021005
JournalJournal of Verification, Validation and Uncertainty Quantification
Issue number2
StatePublished - Jun 2019

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Computer Science Applications
  • Computational Theory and Mathematics


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