Multifidelity computer model emulation with high‐dimensional output: An application to storm surge

Ma, P.; Karagiannis, G.; Konomi, B.A.; Asher, T.G.; Toro, G.R.; Cox, A.T.

Abstract

Hurricane-driven storm surge is one of the most deadly and costly natural disasters, making precise quantification of the surge hazard of great importance. Surge hazard quantification is often performed through physics-based computer models of storm surges. Such computer models can be implemented with a wide range of fidelity levels, with computational burdens varying by several orders of magnitude due to the nature of the system. The threat posed by surge makes greater fidelity highly desirable, however, such models and their high-volume output tend to come at great computational cost, which can make detailed study of coastal flood hazards prohibitive. These needs make the development of an emulator combining high-dimensional output from multiple complex computer models with different fidelity levels important. We propose a parallel partial autoregressive cokriging model to predict highly accurate storm surges in a computationally efficient way over a large spatial domain. This emulator has the capability of predicting storm surges as accurately as a high-fidelity computer model given any storm characteristics over a large spatial domain.

Citation

Ma, P., Karagiannis, G., Konomi, B., Asher, T., Toro, G., & Cox, A. (2022). Multifidelity computer model emulation with high‐dimensional output: An application to storm surge. Journal of the Royal Statistical Society: Series C, 71(4), 861-883. https://doi.org/10.1111/rssc.12558

• Journal Article Type: Article
• Acceptance Date: Feb 19, 2022
• Online Publication Date: Apr 9, 2022
• Publication Date: 2022-08
• Deposit Date: Apr 11, 2022
• Publicly Available Date: Jan 31, 2023
• Journal: Journal of the Royal Statistical Society: Series C (Applied Statistics)
• Print ISSN: 0035-9254
• Electronic ISSN: 1467-9876
• Publisher: Royal Statistical Society
• Peer Reviewed: Peer Reviewed
• Volume: 71
• Issue: 4
• Pages: 861-883
• DOI: https://doi.org/10.1111/rssc.12558
• Public URL: https://durham-repository.worktribe.com/output/1209895
• Related Public URLs: https://arxiv.org/abs/1909.01836