Oates, CJ, Cockayne, J, Aykroyd, RG orcid.org/0000-0003-3700-0816 et al. (1 more author) (2019) Bayesian Probabilistic Numerical Methods in Time-Dependent State Estimation for Industrial Hydrocyclone Equipment. Journal of the American Statistical Association, 114 (528). pp. 1518-1531. ISSN 0162-1459
Abstract
The use of high-power industrial equipment, such as large-scale mixing equipment or a hydrocyclone for separation of particles in liquid suspension, demands careful monitoring to ensure correct operation. The fundamental task of state-estimation for the liquid suspension can be posed as a time-evolving inverse problem and solved with Bayesian statistical methods. In this article, we extend Bayesian methods to incorporate statistical models for the error that is incurred in the numerical solution of the physical governing equations. This enables full uncertainty quantification within a principled computation-precision trade-off, in contrast to the over-confident inferences that are obtained when all sources of numerical error are ignored. The method is cast within a sequential Monte Carlo framework and an optimized implementation is provided in Python.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 American Statistical Association. This is an author produced version of a paper published in Journal of the American Statistical Association. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Electrical tomography, Inverse problems, Partial differential equations, Probabilistic meshless methods, Sequential Monte Carlo |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Jan 2019 11:42 |
Last Modified: | 22 Feb 2020 01:38 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/01621459.2019.1574583 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:140561 |