Equation discovery for nonlinear dynamical systems : a Bayesian viewpoint

Fuentes, R., Nayek, R. orcid.org/0000-0003-4277-8382, Gardner, P. et al. (4 more authors) (2021) Equation discovery for nonlinear dynamical systems : a Bayesian viewpoint. Mechanical Systems and Signal Processing, 154. 107528. ISSN 0888-3270

Abstract

Metadata

Authors/Creators:
Copyright, Publisher and Additional Information: © 2021 Elsevier. 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: Equation discovery; nonlinear system identification; sparse Bayesian learning; Relevance Vector Machine (RVM)
Dates:
  • Accepted: 4 December 2020
  • Published (online): 10 January 2021
  • Published: 1 June 2021
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 Sciences Research CouncilEP/N018427/1; EP/J016942/1; EP/S001565/1
Depositing User: Symplectic Sheffield
Date Deposited: 07 Jan 2021 12:22
Last Modified: 10 Jan 2022 01:38
Status: Published
Publisher: Elsevier
Refereed: Yes
Identification Number: https://doi.org/10.1016/j.ymssp.2020.107528

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