Quantification of Model Uncertainty: Calibration, Model Discrepancy, and Identifiability

Journal of Mechanical Design, Transactions Of the ASME - Tập 134 Số 10 - 2012
Paul D. Arendt1, Daniel W. Apley2, Wei Chen3
1Department of Mechanical Engineering, Northwestern University, 2145 Sheridan Road Room B214, Evanston, IL, 60208
2Department of Industrial Engineering and Management Sciences, Northwestern University, 2145 Sheridan Road Room C150, Evanston, IL, 60208
3Department of Mechanical Engineering, Northwestern University, 2145 Sheridan Road Room A216, Evanston, IL, 60208

Tóm tắt

To use predictive models in engineering design of physical systems, one should first quantify the model uncertainty via model updating techniques employing both simulation and experimental data. While calibration is often used to tune unknown calibration parameters of a computer model, the addition of a discrepancy function has been used to capture model discrepancy due to underlying missing physics, numerical approximations, and other inaccuracies of the computer model that would exist even if all calibration parameters are known. One of the main challenges in model updating is the difficulty in distinguishing between the effects of calibration parameters versus model discrepancy. We illustrate this identifiability problem with several examples, explain the mechanisms behind it, and attempt to shed light on when a system may or may not be identifiable. In some instances, identifiability is achievable under mild assumptions, whereas in other instances, it is virtually impossible. In a companion paper, we demonstrate that using multiple responses, each of which depends on a common set of calibration parameters, can substantially enhance identifiability.

Từ khóa


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Journal of Mechanical Design, Transactions Of the ASME

Tập 134 Số 10

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