Upscaling Uncertainty with Dynamic Discrepancy for a Multi-Scale Carbon Capture System

K. Sham Bhat, David S. Mebane, Priyadarshi Mahapatra, Curtis B. Storlie

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Uncertainties from model parameters and model discrepancy from small-scale models impact the accuracy and reliability of predictions of large-scale systems. Inadequate representation of these uncertainties may result in inaccurate and overconfident predictions during scale-up to larger systems. Hence, multiscale modeling efforts must accurately quantify the effect of the propagation of uncertainties during upscaling. Using a Bayesian approach, we calibrate a small-scale solid sorbent model to thermogravimetric (TGA) data on a functional profile using chemistry-based priors. Crucial to this effort is the representation of model discrepancy, which uses a Bayesian smoothing splines (BSS-ANOVA) framework. Our uncertainty quantification (UQ) approach could be considered intrusive as it includes the discrepancy function within the chemical rate expressions; resulting in a set of stochastic differential equations. Such an approach allows for easily propagating uncertainty by propagating the joint model parameter and discrepancy posterior into the larger-scale system of rate expressions. The broad UQ framework presented here could be applicable to virtually all areas of science where multiscale modeling is used. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)1453-1467
Number of pages15
JournalJournal of the American Statistical Association
Issue number520
StatePublished - Oct 2 2017


  • Bayesian hierarchical modeling
  • Computer model calibration
  • Extrapolation
  • Functional data
  • Propagation of uncertainty

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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