| dc.contributor.author | Che, Yifeng | |
| dc.contributor.author | Yurko, Joseph | |
| dc.contributor.author | Seurin, Paul | |
| dc.contributor.author | Shirvan, Koroush | |
| dc.date.accessioned | 2023-01-24T17:45:54Z | |
| dc.date.available | 2023-01-24T17:45:54Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/147652 | |
| dc.language.iso | en | |
| dc.publisher | Elsevier BV | en_US |
| dc.relation.isversionof | 10.1016/J.ANUCENE.2021.108905 | en_US |
| dc.rights | Creative Commons Attribution-NonCommercial-NoDerivs License | en_US |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | en_US |
| dc.source | arXiv | en_US |
| dc.title | Machine learning-assisted surrogate construction for full-core fuel performance analysis | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Che, Yifeng, Yurko, Joseph, Seurin, Paul and Shirvan, Koroush. 2022. "Machine learning-assisted surrogate construction for full-core fuel performance analysis." Annals of Nuclear Energy, 168. | |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Nuclear Science and Engineering | en_US |
| dc.relation.journal | Annals of Nuclear Energy | en_US |
| dc.eprint.version | Original manuscript | en_US |
| dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
| eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
| dc.date.updated | 2023-01-24T17:42:51Z | |
| dspace.orderedauthors | Che, Y; Yurko, J; Seurin, P; Shirvan, K | en_US |
| dspace.date.submission | 2023-01-24T17:42:55Z | |
| mit.journal.volume | 168 | en_US |
| mit.license | PUBLISHER_CC | |
| mit.metadata.status | Authority Work and Publication Information Needed | en_US |
