Deep learning for inverse material characterisation

Ghaffari Motlagh, Y., Fathi, F., Brigham, J.C. et al. (1 more author) (2025) Deep learning for inverse material characterisation. Computer Methods in Applied Mechanics and Engineering, 436. 117650. ISSN 0045-7825

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Item Type: Article
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© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Keywords: Inverse problem; Direct inversion, PINNs, Material characterization
Dates:
  • Published: 1 March 2025
  • Published (online): 1 January 2025
  • Accepted: 4 December 2024
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Computation Science & Engineering
Depositing User: Symplectic Publications
Date Deposited: 09 Dec 2024 15:06
Last Modified: 14 Jan 2025 16:03
Published Version: https://www.sciencedirect.com/science/article/pii/...
Status: Published
Publisher: Elsevier
Identification Number: 10.1016/j.cma.2024.117650
Open Archives Initiative ID (OAI ID):

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