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Bayesian Emulation for Computer Models with Multiple Partial Discontinuities

Vernon, Ian; Owen, Jonathan; Carter, Jonathan

Bayesian Emulation for Computer Models with Multiple Partial Discontinuities Thumbnail


Authors

Jonathan Owen

Jonathan Carter



Abstract

Computer models are widely used across a range of scientific disciplines to describe various complex physical systems, however to perform full uncertainty quantification we often need to employ emulators. An emulator is a fast statistical construct that mimics the slow to evaluate computer model, and greatly aids the vastly more computationally intensive uncertainty quantification calculations that an important scientific analysis often requires. We examine the problem of emulating computer models that possess multiple, partial discontinuities occurring at known non-linear locations. We introduce the Torn Embedding Non-Stationary Emulation (TENSE) framework, based on carefully designed correlation structures that respect the discontinuities while enabling full exploitation of any smoothness/continuity elsewhere. This leads to a single emulator object that can be updated by all runs simultaneously, and also used for efficient design. This approach avoids having to split the input space into multiple subregions. We apply the TENSE framework to the TNO Challenge II, emulating the OLYMPUS reservoir model, which possesses multiple such discontinuities.

Citation

Vernon, I., Owen, J., & Carter, J. (online). Bayesian Emulation for Computer Models with Multiple Partial Discontinuities. Bayesian Analysis, https://doi.org/10.1214/24-BA1456

Journal Article Type Article
Acceptance Date Jul 7, 2024
Online Publication Date Sep 20, 2024
Deposit Date Sep 10, 2024
Publicly Available Date Oct 2, 2024
Journal Bayesian Analysis
Print ISSN 1936-0975
Electronic ISSN 1931-6690
Publisher International Society for Bayesian Analysis (ISBA)
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1214/24-BA1456
Public URL https://durham-repository.worktribe.com/output/2853012
Publisher URL https://projecteuclid.org/journals/bayesian-analysis

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