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Predicting extreme events from data using deep machine learning : when and where

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dc.contributor.authorJiang, Junjie
dc.contributor.authorHuang, Zi-Gang
dc.contributor.authorGrebogi, Celso
dc.contributor.authorLai, Ying-Cheng
dc.contributor.institutionUniversity of Aberdeen.Institute for Complex Systems and Mathematical Biology (ICSMB)en
dc.contributor.institutionUniversity of Aberdeen.Physicsen
dc.date.accessioned2022-04-12T08:37:01Z
dc.date.available2022-04-12T08:37:01Z
dc.date.issued2022-06-01
dc.descriptionACKNOWLEDGMENTS The work at Arizona State University was supported by AFOSR under Grant No. FA9550-21-1-0438 and by ONR under Grant No. N00014-21-1-2323. The work at Xi’an Jiaotong University was supported by the National Key R&D Program of China (Grant No. 2021ZD0201300), National Natural Science Foundation of China (Grant No. 11975178), and K. C. Wong Education Foundation.en
dc.description.statusPeer revieweden
dc.format.extent14
dc.format.extent7095520
dc.identifier214043111
dc.identifiera208a913-faa8-4118-b7a9-f631e6b88645
dc.identifier85128823138
dc.identifier.citationJiang, J, Huang, Z-G, Grebogi, C & Lai, Y-C 2022, 'Predicting extreme events from data using deep machine learning : when and where', Physical Review Research, vol. 4, no. 2, 023028. https://doi.org/10.1103/PhysRevResearch.4.023028en
dc.identifier.doi10.1103/PhysRevResearch.4.023028
dc.identifier.iss2en
dc.identifier.issn2643-1564
dc.identifier.otherArXiv: http://arxiv.org/abs/2203.17155v1
dc.identifier.otherORCID: /0000-0002-9811-4617/work/111385184
dc.identifier.urihttps://hdl.handle.net/2164/18388
dc.identifier.vol4en
dc.language.isoeng
dc.relation.ispartofPhysical Review Researchen
dc.subjectSubstantive connection via an eligible employment contracten
dc.subjectQC Physicsen
dc.subject.lccQCen
dc.titlePredicting extreme events from data using deep machine learning : when and whereen
dc.typeJournal articleen

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