Calibration of computational models with categorical parameters and correlated outputs via Bayesian smoothing spline ANOVA
Files
Accepted manuscript
Date
2015-03-01
Authors
Storlie, Curtis B.
Lane, William A.
Ryan, Emily M.
Gattiker, James R.
Higdon, David M.
Version
OA Version
Citation
Curtis B Storlie, William A Lane, Emily M Ryan, James R Gattiker, David M Higdon. 2015. "Calibration of Computational Models With Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA." JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, v. 110, Issue 509, pp. 68 - 82 (15).
Abstract
It has become commonplace to use complex computer models to predict outcomes in regions where data do not exist. Typically these models
need to be calibrated and validated using some experimental data, which often consists of multiple correlated outcomes. In addition, some
of the model parameters may be categorical in nature, such as a pointer variable to alternate models (or submodels) for some of the physics
of the system. Here, we present a general approach for calibration in such situations where an emulator of the computationally demanding
models and a discrepancy term from the model to reality are represented within a Bayesian smoothing spline (BSS) ANOVA framework.
The BSS-ANOVA framework has several advantages over the traditional Gaussian process, including ease of handling categorical inputs
and correlated outputs, and improved computational efficiency. Finally, this framework is then applied to the problem that motivated its
design; a calibration of a computational fluid dynamics (CFD) model of a bubbling fluidized which is used as an absorber in a CO2 capture
system. Supplementary materials for this article are available online.
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License
© 2015 American Statistical Association
Journal of the American Statistical Association