Improving identifiability in model calibration using multiple responses

Paul D. Arendt, Daniel Apley, Wei Chen*, David Lamb, David Gorsich

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

73 Scopus citations

Abstract

In physics-based engineering modeling, the two primary sources of model uncertainty, which account for the differences between computer models and physical experiments, are parameter uncertainty and model discrepancy. Distinguishing the effects of the two sources of uncertainty can be challenging. For situations in which identifiability cannot be achieved using only a single response, we propose to improve identifiability by using multiple responses that share a mutual dependence on a common set of calibration parameters. To that end, we extend the single response modular Bayesian approach for calculating posterior distributions of the calibration parameters and the discrepancy function to multiple responses. Using an engineering example, we demonstrate that including multiple responses can improve identifiability (as measured by posterior standard deviations) by an amount that ranges from minimal to substantial, depending on the characteristics of the specific responses that are combined.

Original languageEnglish (US)
Article number100909
JournalJournal of Mechanical Design, Transactions Of the ASME
Volume134
Issue number10
DOIs
StatePublished - Oct 8 2012

Keywords

  • Gaussian process
  • Multiple response emulator
  • calibration
  • identifiability
  • model updating
  • multiple responses
  • uncertainty quantification

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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