Robust equation discovery considering model discrepancy: a sparse Bayesian and Gaussian process approach

Zhu, Y.-C., Gardner, P. orcid.org/0000-0002-1882-9728, Wagg, D.J. et al. (3 more authors) (2022) Robust equation discovery considering model discrepancy: a sparse Bayesian and Gaussian process approach. Mechanical Systems and Signal Processing, 168. 108717. ISSN 0888-3270

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2021 Elsevier Ltd. This is an author produced version of a paper subsequently published in Mechanical Systems and Signal Processing. 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: Parameter identification; Sparse Bayesian inference; Gaussian process; Model discrepancy; Equation discovery
Dates:
  • Accepted: 30 November 2021
  • Published (online): 13 December 2021
  • Published: 1 April 2022
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield)
Funding Information:
FunderGrant number
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCILEP/R006768/1
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCILEP/S001565/1
Depositing User: Symplectic Sheffield
Date Deposited: 14 Dec 2021 15:16
Last Modified: 13 Dec 2022 01:13
Status: Published
Publisher: Elsevier BV
Refereed: Yes
Identification Number: https://doi.org/10.1016/j.ymssp.2021.108717

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