Professor Ian Vernon i.r.vernon@durham.ac.uk
Professor
Bayesian Emulation for Computer Models with Multiple Partial Discontinuities
Vernon, Ian; Owen, Jonathan; Carter, Jonathan
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 |
Files
Published Journal Article (Advance Online Version)
(3.2 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Version
Advance Online Version
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