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Flexible multi-fidelity framework for load estimation of wind farms through graph neural networks and transfer learning – ERRATUM

Published online by Cambridge University Press:  03 February 2025

Gregory Duthé Open the ORCID record for Gregory Duthé [Opens in a new window] ,
Francisco de N Santos ,
Imad Abdallah ,
Wout Weijtjens Open the ORCID record for Wout Weijtjens [Opens in a new window] ,
Christof Devrient  and
Eleni Chatzi Open the ORCID record for Eleni Chatzi [Opens in a new window]

Abstract

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Type
Erratum
Information
Data-Centric Engineering , Volume 6 , 2025 , e10
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Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction, provided the original article is properly cited.
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Copyright
© The Author(s), 2025. Published by Cambridge University Press

In Section 2 of this article (Methodological Overview), the publisher introduced a formatting error in the citation of Duthé et al (2023b). The section reads:

“We build upon the methodology described in Duthàet al. (2023b) and in de N Santos et al. (2024a), wherein PyWake aeroelastic simulations are used to train and validate GNNs.”

It should read:

“We build upon the methodology described in Duthé et al. (2023b) and in de N Santos et al. (2024a), wherein PyWake aeroelastic simulations are used to train and validate GNNs.”

The publisher apologises to the author for the error.

References

Duthé, G, de N Santos, F, Abdallah, I, Weijtjens, W, Devriendt, C, Chatzi, E. Flexible multi-fidelity framework for load estimation of wind farms through graph neural networks and transfer learning. Data-Centric Engineering. 2024;5:e29. https://doi.org/10.1017/dce.2024.35CrossRefGoogle Scholar
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