Bayesian analysis of multifidelity computer models with local features and non-nested experimental designs: Application to the WRF model

Konomi, B.; Karagiannis, G.

Abstract

Motivated by a multi-fidelity Weather Research and Forecasting (WRF) climate model application where the available simulations are not generated based on hierarchically nested experimental design, we develop a new co-kriging procedure called Augmented Bayesian Treed Co-Kriging. The proposed procedure extends the scope of co-kriging in two major ways. We introduce a binary treed partition latent process in the multifidelity setting to account for non-stationary and potential discontinuities in the model outputs at different fidelity levels. Moreover, we introduce an efficient imputation mechanism which allows the practical implementation of co-kriging when the experimental design is non-hierarchically nested by enabling the specification of semi-conjugate priors. Our imputation strategy allows the design of an efficient RJ-MCMC implementation that involves collapsed blocks and direct simulation from conditional distributions. We develop the Monte Carlo recursive emulator which provides a Monte Carlo proxy for the full predictive distribution of the model output at each fidelity level, in a computationally feasible manner. The performance of our method is demonstrated on benchmark examples and used for the analysis of a large-scale climate modeling application which involves the WRF model.

Citation

Konomi, B., & Karagiannis, G. (2021). Bayesian analysis of multifidelity computer models with local features and non-nested experimental designs: Application to the WRF model. Technometrics, 63(4), 510-522. https://doi.org/10.1080/00401706.2020.1855253

• Journal Article Type: Article
• Acceptance Date: Nov 16, 2020
• Online Publication Date: Dec 7, 2020
• Publication Date: 2021
• Deposit Date: Nov 16, 2020
• Publicly Available Date: Dec 7, 2021
• Journal: Technometrics
• Print ISSN: 0040-1706
• Electronic ISSN: 1537-2723
• Publisher: American Statistical Association
• Peer Reviewed: Peer Reviewed
• Volume: 63
• Issue: 4
• Pages: 510-522
• DOI: https://doi.org/10.1080/00401706.2020.1855253
• Public URL: https://durham-repository.worktribe.com/output/1250958
• Related Public URLs: https://arxiv.org/abs/1910.08063