Multi-response Approach to Improving Identifiability in Model Calibration

Zhen Jiang (), Paul D. Arendt (), Daniel W. Apley () and Wei Chen ()
Additional contact information
Zhen Jiang: Northwestern University, Department of Mechanical Engineering
Paul D. Arendt: CNA Financial Corporation
Daniel W. Apley: Northwestern University, Department of Industrial Engineering and Management Sciences
Wei Chen: Northwestern University, Department of Mechanical Engineering

Chapter 4 in Handbook of Uncertainty Quantification, 2017, pp 69-127 from Springer

Abstract: Abstract In physics-based engineering modeling, two primary sources of model uncertainty that account for the differences between computer models and physical experiments are parameter uncertainty and model discrepancy. One of the main challenges in model updating results from the difficulty in distinguishing between the effects of calibration parameters versus model discrepancy. In this chapter, this identifiability problem is illustrated with several examples that explain the mechanisms behind it and that attempt to shed light on when a system may or may not be identifiable. For situations in which identifiability cannot be achieved using only a single response, an approach is developed to improve identifiability by using multiple responses that share a mutual dependence on the calibration parameters. Furthermore, prior to conducting physical experiments but after conducting computer simulations, in order to address the issue of how to select the most appropriate set of responses to measure experimentally to best enhance identifiability, a preposterior analysis approach is presented to predict the degree of identifiability that will result from using different sets of responses to measure experimentally. To handle the computational challenges of the preposterior analysis, we also present a surrogate preposterior analysis based on the Fisher information of the calibration parameters.

Keywords: Parameter uncertainty; Model discrepancy; Experimental uncertainty; Calibration; Bias correction; (Non)identifiability; Identifiability; Model uncertainty quantification; Calibration parameters; Discrepancy function; Gaussian process; Modular Bayesian approach; Hyperparameters; Simply supported beam; Non-informative prior; Multi-response Gaussian process; Multi-response modular Bayesian approach; Spatial correlation; Non-spatial covariance; Preposterior covariance; Preposterior analysis; Fixed-θ preposterior analysis; Surrogate preposterior analysis; Observed Fisher information (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-12385-1_65

Ordering information: This item can be ordered from
http://www.springer.com/9783319123851

DOI: 10.1007/978-3-319-12385-1_65

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().