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

Jiang, JunjieHuang, Zi-GangGrebogi, CelsoLai, Ying-Cheng2022-04-122022-04-122022-06-01Jiang, 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.4.0230282643-1564ArXiv: http://arxiv.org/abs/2203.17155v1ORCID: /0000-0002-9811-4617/work/111385184https://hdl.handle.net/2164/18388ACKNOWLEDGMENTS 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.147095520engSubstantive connection via an eligible employment contractQC PhysicsQCPredicting extreme events from data using deep machine learning : when and whereJournal article10.1103/PhysRevResearch.4.02302842