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Bayesian treed multivariate Gaussian process with adaptive design: Application to a carbon capture unit

Konomi, B.; Karagiannis, G.; Sarkar, A.; Sun, X.; Lin, G.

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Authors

B. Konomi

A. Sarkar

X. Sun

G. Lin



Abstract

Computer experiments are widely used in scientific research to study and predict the behavior of complex systems, which often have responses consisting of a set of nonstationary outputs. The computational cost of simulations at high resolution often is expensive and impractical for parametric studies at different input values. In this article, we develop a Bayesian treed multivariate Gaussian process (BTMGP) as an extension of the Bayesian treed Gaussian process (BTGP) to model the cross-covariance function and the nonstationarity of the multivariate output. We facilitate the computational complexity of the Markov chain Monte Carlo sampler by choosing appropriately the covariance function and prior distributions. Based on the BTMGP, we develop a sequential design of experiment for the input space and construct an emulator. We demonstrate the use of the proposed method in test cases and compare it with alternative approaches. We also apply the sequential sampling technique and BTMGP to model the multiphase flow in a full scale regenerator of a carbon capture unit.

Citation

Konomi, B., Karagiannis, G., Sarkar, A., Sun, X., & Lin, G. (2014). Bayesian treed multivariate Gaussian process with adaptive design: Application to a carbon capture unit. Technometrics, 56(2), 145-158. https://doi.org/10.1080/00401706.2013.879078

Journal Article Type Article
Acceptance Date May 16, 2014
Online Publication Date May 16, 2014
Publication Date Apr 3, 2014
Deposit Date Nov 10, 2016
Publicly Available Date Mar 29, 2024
Journal Technometrics
Print ISSN 0040-1706
Electronic ISSN 1537-2723
Publisher American Statistical Association
Peer Reviewed Peer Reviewed
Volume 56
Issue 2
Pages 145-158
DOI https://doi.org/10.1080/00401706.2013.879078

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