Smyl, D. orcid.org/0000-0002-6730-5277, Tallman, T.N., Black, J.A. orcid.org/0000-0002-3529-6708 et al. (2 more authors) (2021) Learning and correcting non-Gaussian model errors. Journal of Computational Physics, 432. 110152. ISSN 0021-9991
Abstract
All discretized numerical models contain modeling errors – this reality is amplified when reduced-order models are used. The ability to accurately approximate modeling errors informs statistics on model confidence and improves quantitative results from frameworks using numerical models in prediction, tomography, and signal processing. Further to this, the compensation of highly nonlinear and non-Gaussian modeling errors, arising in many ill-conditioned systems aiming to capture complex physics, is a historically difficult task. In this work, we address this challenge by proposing a neural network approach capable of accurately approximating and compensating for such modeling errors in augmented direct and inverse problems. The viability of the approach is demonstrated using simulated and experimental data arising from differing physical direct and inverse problems.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier Inc. This is an author produced version of a paper subsequently published in Journal of Computational Physics. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Finite element method; Inverse problems; Model errors; Neural networks; Non-linearity; Tomography |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Civil and Structural Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Feb 2021 12:53 |
Last Modified: | 29 Jan 2022 01:38 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.jcp.2021.110152 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:171504 |
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