Towards a population-informed approach to the definition of data-driven models for structural dynamics

Tsialiamanis, G. orcid.org/0000-0002-1205-4175, Dervilis, N. orcid.org/0000-0002-5712-7323, Wagg, D.J. orcid.org/0000-0002-7266-2105 et al. (1 more author) (2023) Towards a population-informed approach to the definition of data-driven models for structural dynamics. Mechanical Systems and Signal Processing, 200. 110581. ISSN 0888-3270

Abstract

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Item Type: Article
Authors/Creators:
Copyright, Publisher and Additional Information:

© 2023 The Authors. Except as otherwise noted, this author-accepted version of a journal article published in Mechanical Systems and Signal Processing is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/

Keywords: Structural dynamics; Machine learning; Population-based modelling; Transfer learning; Meta-learning
Dates:
  • Published: 1 October 2023
  • Published (online): 14 July 2023
  • Accepted: 29 June 2023
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield)
Funding Information:
Funder
Grant number
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL
EP/R006768/1
Depositing User: Symplectic Sheffield
Date Deposited: 04 Aug 2023 10:59
Last Modified: 04 Sep 2023 11:21
Status: Published
Publisher: Elsevier BV
Refereed: Yes
Identification Number: 10.1016/j.ymssp.2023.110581
Open Archives Initiative ID (OAI ID):

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