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Title: A Theoretical Framework for Calibration in Computer Models: Parametrization, Estimation and Convergence Properties

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 L 2-consistency for calibration as a justification for calibration methods. It is shown that a simplified version of the original KO method leads to asymptotically L 2-inconsistent calibration. This L 2-inconsistency can be remedied by modifying the original estimation procedure. A novel calibration method, called the L 2 calibration, is proposed and proven to be L 2-consistent and enjoys optimal convergence rate. Furthermore a numerical example and some mathematical analysis are used to illustrate the source of the L 2-inconsistency problem.
Authors:
 [1] ;  [2]
  1. Chinese Academy of Sciences (CAS), Beijing (China)
  2. Georgia Inst. of Technology, Atlanta, GA (United States)
Publication Date:
Report Number(s):
DOE-GT-0010548-2
Journal ID: ISSN 2166-2525; FG02-13ER26159
Grant/Contract Number:
SC0010548
Type:
Accepted Manuscript
Journal Name:
SIAM/ASA Journal on Uncertainty Quantification
Additional Journal Information:
Journal Volume: 4; Journal Issue: 1; Journal ID: ISSN 2166-2525
Publisher:
SIAM
Research Org:
Georgia Tech Research Corp., Atlanta, GA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; computer experiments; uncertainty quantification; Gaussian process; reproducing kernel Hilbert space
OSTI Identifier:
1405142