In physics-based engineering modeling and uncertainty quantification, distinguishing the effects of two main sources of uncertainty — calibration parameter uncertainty and model discrepancy — is challenging. Previous research has shown that identifiability can sometimes be improved by experimentally measuring multiple responses of the system that share a mutual dependence on a common set of calibration parameters. In this paper, we address the issue of how to select the most appropriate subset of responses to measure experimentally, to best enhance identifiability. We propose a preposterior analysis approach that, prior to conducting the physical experiments but after conducting computer simulations, can predict the degree of identifiability that will result using different subsets of responses to measure experimentally. We quantify identifiability via the posterior covariance of the calibration parameters, and predict it via the preposterior covariance from a modular Bayesian Monte Carlo analysis of a multi-response Gaussian process model. The proposed method is applied to a simply supported beam example to select two out of six responses to best improve identifiability. The estimated preposterior covariance is compared to the actual posterior covariance to demonstrate the effectiveness of the method.
Skip Nav Destination
ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
August 4–7, 2013
Portland, Oregon, USA
Conference Sponsors:
- Design Engineering Division
- Computers and Information in Engineering Division
ISBN:
978-0-7918-5589-8
PROCEEDINGS PAPER
Preposterior Analysis to Select Experimental Responses for Improving Identifiability in Model Uncertainty Quantification
Zhen Jiang,
Zhen Jiang
Northwestern University, Evanston, IL
Search for other works by this author on:
Daniel W. Apley
Daniel W. Apley
Northwestern University, Evanston, IL
Search for other works by this author on:
Zhen Jiang
Northwestern University, Evanston, IL
Wei Chen
Northwestern University, Evanston, IL
Daniel W. Apley
Northwestern University, Evanston, IL
Paper No:
DETC2013-12457, V03BT03A051; 12 pages
Published Online:
February 12, 2014
Citation
Jiang, Z, Chen, W, & Apley, DW. "Preposterior Analysis to Select Experimental Responses for Improving Identifiability in Model Uncertainty Quantification." Proceedings of the ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 3B: 39th Design Automation Conference. Portland, Oregon, USA. August 4–7, 2013. V03BT03A051. ASME. https://doi.org/10.1115/DETC2013-12457
Download citation file:
8
Views
0
Citations
Related Proceedings Papers
Related Articles
Uncertainty Quantification for Additive Manufacturing Process Improvement: Recent Advances
ASME J. Risk Uncertainty Part B (March,2022)
Uncertainty Quantification for Fatigue Life of Offshore Wind Turbine Structure
ASME J. Risk Uncertainty Part B (December,2021)
Predictive Modeling and Uncertainty Quantification of Laser Shock Processing by Bayesian Gaussian Processes With Multiple Outputs
J. Manuf. Sci. Eng (August,2014)
Related Chapters
Advances in the Stochastic Modeling of Constitutive Laws at Small and Finite Strains
Advances in Computers and Information in Engineering Research, Volume 2
Computational Modeling of Dynamic Planing Forces
Proceedings of the 10th International Symposium on Cavitation (CAV2018)
Microstructure Evolution and Physics-Based Modeling
Ultrasonic Welding of Lithium-Ion Batteries