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An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics
Helin Gong 
,
Sibo Cheng ,
Zhang Chen,
Qing Li,
César Quilodrán-Casas,
Dunhui Xiao ,
Rossella Arcucci
,
Sibo Cheng ,
Zhang Chen,
Qing Li,
César Quilodrán-Casas,
Dunhui Xiao ,
Rossella Arcucci
Annals of Nuclear Energy, Volume: 179, Start page: 109431
Swansea University Author:
Dunhui Xiao
-
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DOI (Published version): 10.1016/j.anucene.2022.109431
Abstract
An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics
| Published in: | Annals of Nuclear Energy |
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| ISSN: | 0306-4549 |
| Published: |
Elsevier BV
2022
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa61228 |
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| Keywords: |
Operational digital twins; Machine learning; Latent assimilation; SVD-autoencoder; Nuclear reactor physics |
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| College: |
Faculty of Science and Engineering |
| Funders: |
This work is supported by the National Natural Science Foundation of China (Grant No. 11905216, Grant No. 12175220), and the Stability Support Fund for National Defence and Science and Technology on Reactor System Design Technology Laboratory. This research is partially funded by the Leverhulme Centre for Wildfires, Environment and Society through the Leverhulme Trust, Grant No. RC-2018-023. This work is partially supported by the EP/T000414/1 PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems (PREMIERE). |
| Start Page: |
109431 |