In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@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}
}
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 -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
Email us.
Sign up to our mailing list.
Follow us on Twitter, LinkedIn and Instagram.
