Local flow estimation at the top of the Earth’s core using Physics Informed Neural Networks

Shakespeare-Rees, N. orcid.org/0000-0003-1193-9788, Livermore, P.W. orcid.org/0000-0001-7591-6716, Davies, C.J. et al. (4 more authors) (2025) Local flow estimation at the top of the Earth’s core using Physics Informed Neural Networks. Physics of the Earth and Planetary Interiors, 367. 107424. ISSN: 0031-9201

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Item Type: Article
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© 2025 The Authors. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.

Keywords: Physics Informed Neural Networks (PINNs), Regional models, Secular variation, Outer core flow, Core–Mantle Boundary, Earth’s core
Dates:
  • Accepted: 4 August 2025
  • Published (online): 26 August 2025
  • Published: October 2025
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 04 Sep 2025 14:35
Last Modified: 04 Sep 2025 14:35
Status: Published
Publisher: Elsevier
Identification Number: 10.1016/j.pepi.2025.107424
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