{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T04:19:45Z","timestamp":1769401185052,"version":"3.49.0"},"reference-count":30,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11601329"],"award-info":[{"award-number":["11601329"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11771289"],"award-info":[{"award-number":["11771289"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013287","name":"Science Challenge Project","doi-asserted-by":"crossref","award":["TZ2018001"],"award-info":[{"award-number":["TZ2018001"]}],"id":[{"id":"10.13039\/501100013287","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/access.2019.2934980","type":"journal-article","created":{"date-parts":[[2019,8,15]],"date-time":"2019-08-15T15:45:01Z","timestamp":1565883901000},"page":"112087-112096","source":"Crossref","is-referenced-by-count":11,"title":["A Hierarchical Neural Hybrid Method for Failure Probability Estimation"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5627-2932","authenticated-orcid":false,"given":"Ke","family":"Li","sequence":"first","affiliation":[]},{"given":"Kejun","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Jinglai","family":"Li","sequence":"additional","affiliation":[]},{"given":"Tianfan","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Qifeng","family":"Liao","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1093\/imanum\/drn014"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2915350"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2880010"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2010.08.022"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/S0266-8920(01)00019-4"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1002\/wics.73"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2011.08.008"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(91)90009-T"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref19","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/0167-4730(87)90002-6"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-3094-6_4"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-4730(97)00026-X"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1137\/120891393"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/0167-4730(90)90012-E"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/0167-4730(93)90003-J"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1137\/050645142"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/0167-4730(89)90003-9"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-1771-9_4"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780199678792.001.0001"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90020-8"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.2307\/2280232"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.2514\/6.2014-0802"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.2514\/6.2017-1094"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.3389\/fmats.2019.00061"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/0167-4730(93)90056-7"},{"key":"ref23","article-title":"A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems","author":"meng","year":"2019","journal-title":"arXiv 1903 00104"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1002\/fld.3697"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2016.04.029"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8600701\/08801846.pdf?arnumber=8801846","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T11:32:37Z","timestamp":1641987157000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8801846\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2934980","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]}}}