Imperial College London

DrRossellaArcucci

Faculty of EngineeringDepartment of Earth Science & Engineering

Senior Lecturer in Data Science and Machine Learning
 
 
 
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Contact

 

r.arcucci Website

 
 
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Location

 

Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Gong:2022:10.1016/j.anucene.2022.109431,
author = {Gong, H and Cheng, S and Chen, Z and Li, Q and Quilodran-Casas, C and Xiao, D and Arcucci, R},
doi = {10.1016/j.anucene.2022.109431},
journal = {ANNALS OF NUCLEAR ENERGY},
title = {An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics},
url = {http://dx.doi.org/10.1016/j.anucene.2022.109431},
volume = {179},
year = {2022}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AU - Gong,H
AU - Cheng,S
AU - Chen,Z
AU - Li,Q
AU - Quilodran-Casas,C
AU - Xiao,D
AU - Arcucci,R
DO - 10.1016/j.anucene.2022.109431
PY - 2022///
SN - 0306-4549
TI - An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics
T2 - ANNALS OF NUCLEAR ENERGY
UR - http://dx.doi.org/10.1016/j.anucene.2022.109431
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000861581100006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
VL - 179
ER -