Gardner, P., Rogers, T. orcid.org/0000-0002-3433-3247, Lord, C. orcid.org/0000-0002-2470-098X et al. (1 more author) (2019) Learning of model discrepancy for structural dynamics applications using Bayesian history matching. In: Journal of Physics: Conference Series. XIII International Conference on Recent Advances in Structural Dynamics (RASD 2019), 15-17 Apr 2019, Valpre, France. IOP Publishing
Abstract
Calibration of computer models for structural dynamics is often an important task in creating valid predictions that match observational data. However, calibration alone will lead to biased estimates of system parameters when a mechanism for model discrepancy is not included. The definition of model discrepancy is the mismatch between observational data and the model when the 'true' parameters are known. This will occur due to the absence and/or simplification of certain physics in the computer model. Bayesian History Matching (BHM) is a 'likelihood-free' method for obtaining calibrated outputs whilst accounting for model discrepancies, typically via an additional variance term. The approach assesses the input space, using an emulator of the complex computer model, and identifies parameter sets that could have plausibly generated the target outputs. In this paper a more informative methodology is outlined where the functional form of the model discrepancy is inferred, improving predictive performance. The algorithm is applied to a case study for a representative five storey building structure with the objective of calibrating outputs of a finite element (FE) model. The results are discussed with appropriate validation metrics that consider the complete distribution.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IOP Publishing. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence (https://creativecommons.org/licenses/by/3.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Sep 2019 14:38 |
Last Modified: | 04 Sep 2019 14:38 |
Status: | Published |
Publisher: | IOP Publishing |
Refereed: | Yes |
Identification Number: | 10.1088/1742-6596/1264/1/012052 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:150432 |
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