A Theoretical Framework for Calibration in Computer Models: Parametrization, Estimation and Convergence Properties
- Chinese Academy of Sciences (CAS), Beijing (China)
- Georgia Inst. of Technology, Atlanta, GA (United States)
Calibration parameters in deterministic computer experiments are those attributes that cannot be measured or available in physical experiments. Here, an approach to estimate them by using data from physical experiments and computer simulations. A theoretical framework is given which allows us to study the issues of parameter identifiability and estimation. We define the L2-consistency for calibration as a justification for calibration methods. It is shown that a simplified version of the original KO method leads to asymptotically L2-inconsistent calibration. This L2-inconsistency can be remedied by modifying the original estimation procedure. A novel calibration method, called the L2 calibration, is proposed and proven to be L2-consistent and enjoys optimal convergence rate. Furthermore a numerical example and some mathematical analysis are used to illustrate the source of the L2-inconsistency problem.
- Research Organization:
- Georgia Institute of Technology, Atlanta, GA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- Grant/Contract Number:
- SC0010548
- OSTI ID:
- 1405142
- Report Number(s):
- DOE-GT-0010548-2; FG02-13ER26159
- Journal Information:
- SIAM/ASA Journal on Uncertainty Quantification, Vol. 4, Issue 1; ISSN 2166-2525
- Publisher:
- SIAMCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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