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Discussing the spectrum of physics-enhanced machine learning: a survey on structural mechanics applications – CORRIGENDUM

Published online by Cambridge University Press:  21 January 2025

Marcus Haywood-Alexander Open the ORCID record for Marcus Haywood-Alexander [Opens in a new window] ,
Wei Liu ,
Kiran Bacsa ,
Zhilu Lai Open the ORCID record for Zhilu Lai [Opens in a new window]  and
Eleni Chatzi Open the ORCID record for Eleni Chatzi [Opens in a new window]

Abstract

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Corrigendum
Information
Data-Centric Engineering , Volume 6 , 2025 , e8
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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 in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press

The authors have informed us of a correction to the acknowledgments and funding statement in the article. The statements originally published in the article read:

Acknowledgments

The research was conducted as part of the Future Resilient Systems (FRS) program at the Singapore-ETH Centre, which was established collaboratively between ETH Zurich and the National Research Foundation Singapore. The authors gratefully acknowledge the funding from the Swiss National Science Foundation (SNSF) under the Horizon Europe funding guarantee, for the project “ReCharged—Climate-aware Resilience for Sustainable Critical and interdependent Infrastructure Systems enhanced by emerging Digital Technologies” (grant agreement No: 101086413).

Funding statement

This research was supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program. This work received no specific grant from any funding agency, commercial or not-for-profit sectors.

The corrected statements are below, and the article has been updated accordingly.

Acknowledgment

The research was conducted as part of the Future Resilient Systems (FRS) program at the Singapore-ETH Centre, which was established collaboratively between ETH Zurich and the National Research Foundation Singapore.

Funding statement

This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. The authors gratefully acknowledge the funding from the State Secretariat for Education, Research, and Innovation (SERI) as matching funding for the Horizon Europe project ‘ReCharged - Climate-aware Resilience for Sustainable Critical and interdependent Infrastructure Systems enhanced by emerging Digital Technologies’ (grant agreement No: 101086413).

References

Haywood-Alexander, M, Liu, W, Bacsa, K, Lai, Z, Chatzi, E (2024) Discussing the spectrum of physics-enhanced machine learning: a survey on structural mechanics applications. Data-Centric Engineering 5, e30. https://doi.org/10.1017/dce.2024.33CrossRefGoogle Scholar
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