On the Statistical Calibration of Physical Models
ABSTRACT We introduce a novel statistical calibration framework for physical models, relying on probabilistic embedding of model discrepancy error within the model. For clarity of illustration, we take the measurement errors out of consideration, calibrating a chemical model of interest with respect...
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Published in | International journal of chemical kinetics Vol. 47; no. 4; pp. 246 - 276 |
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Main Authors | , , |
Format | Journal Article |
Language | English French German |
Published |
Hoboken
Blackwell Publishing Ltd
01.04.2015
Wiley Subscription Services, Inc Wiley Blackwell (John Wiley & Sons) |
Subjects | |
Online Access | Get full text |
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Summary: | ABSTRACT
We introduce a novel statistical calibration framework for physical models, relying on probabilistic embedding of model discrepancy error within the model. For clarity of illustration, we take the measurement errors out of consideration, calibrating a chemical model of interest with respect to a more detailed model, considered as “truth” for the present purpose. We employ Bayesian statistical methods for such model‐to‐model calibration and demonstrate their capabilities on simple synthetic models, leading to a well‐defined parameter estimation problem that employs approximate Bayesian computation. The method is then demonstrated on two case studies for calibration of kinetic rate parameters for methane air chemistry, where ignition time information from a detailed elementary‐step kinetic model is used to estimate rate coefficients of a simple chemical mechanism. We show that the calibrated model predictions fit the data and that uncertainty in these predictions is consistent in a mean‐square sense with the discrepancy from the detailed model data. |
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Bibliography: | ArticleID:KIN20906 istex:FC6E644411D6F18261F18A5C9B3DE04A2101A6B8 ark:/67375/WNG-5CGL6PLL-1 USDOE |
ISSN: | 0538-8066 1097-4601 |
DOI: | 10.1002/kin.20906 |