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Making global sensitivity analysis feasible using neural network surrogates – ERRATUM

Published online by Cambridge University Press:  18 December 2025

Gihan Weerasinghe Open the ORCID record for Gihan Weerasinghe [Opens in a new window] ,
Ramaseshan Kannan  and
Samila Bandara
Gihan Weerasinghe
Affiliation:
Digital Technology Group , Arup, Manchester, UK
Ramaseshan Kannan
Affiliation:
Digital Technology Group , Arup, Manchester, UK
Samila Bandara
Affiliation:
Digital Technology Group , Arup, London, UK

Abstract

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Keywords

finite element modelsgeotechnical analysisglobal sensitivity analysisstructural analysis

Information

Type
Erratum
Information
Data-Centric Engineering , Volume 6 , 2025 , e55
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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 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press

There was an error in the citation details of this article (Weerasinghe G, 2025), as originally published. The Volume number provided in the citation (7) was incorrect. The article was published in 2025 and is therefore part of Volume 6 of Data-Centric Engineering. The DOI and all other details in the citation were correct.

The article has now been updated to reflect the correct Volume number, as per the reference below. The publisher apologises to the authors and any readers for confusion caused by the error.

References

Weerasinghe, G, Kannan, R and Bandara, S (2025) Making global sensitivity analysis feasible using neural network surrogates. Data-Centric Engineering. 6:e1. https://doi.org/10.1017/dce.2025.10029.CrossRefGoogle Scholar
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