{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T08:18:04Z","timestamp":1778314684468,"version":"3.51.4"},"reference-count":35,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Key Research and Development Program of Hubei Province","award":["2021AAB001"],"award-info":[{"award-number":["2021AAB001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3422224","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T19:35:13Z","timestamp":1719948913000},"page":"92130-92141","source":"Crossref","is-referenced-by-count":9,"title":["An Adaptive Sampling Method Based on Expected Improvement Function and Residual Gradient in PINNs"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1703-5204","authenticated-orcid":false,"given":"Yanbing","family":"Liu","sequence":"first","affiliation":[{"name":"School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liping","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8804-6553","authenticated-orcid":false,"given":"Jianwan","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8860-2839","authenticated-orcid":false,"given":"Yu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/7068349"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-81-322-3972-7_19"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1137\/1010073"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1002\/num.22373"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ast.2021.106681"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2021.100029"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2022.115810"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s10409-021-01148-1"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3390\/w13040423"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3390\/e25040674"},{"key":"ref12","article-title":"FiniteNet: A fully convolutional LSTM network architecture for time-dependent partial differential equations","author":"Stevens","year":"2020","journal-title":"arXiv:2002.03014"},{"key":"ref13","article-title":"Physics-informed attention-based neural network for solving non-linear partial differential equations","author":"Rodriguez-Torrado","year":"2021","journal-title":"arXiv:2105.07898"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2020.113028"},{"key":"ref15","first-page":"1","article-title":"Extended physics-informed neural networks (XPINNs): A generalized space-time domain decomposition based deep learning framework for nonlinear partial differential equations","volume-title":"Proc. AAAI Spring Symp.","volume":"10","author":"Jagtap"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1137\/20m1318043"},{"key":"ref17","article-title":"Multi-objective loss balancing for physics-informed deep learning","author":"Bischof","year":"2021","journal-title":"arXiv:2110.09813"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.05.015"},{"key":"ref19","article-title":"Temporal consistency loss for physics-informed neural networks","author":"Thakur","year":"2023","journal-title":"arXiv:2301.13262"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2019.112789"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1137\/19m1274067"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2022.115100"},{"key":"ref23","article-title":"RANG: A residual-based adaptive node generation method for physics-informed neural networks","author":"Peng","year":"2022","journal-title":"arXiv:2205.01051"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2022.115671"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s10483-023-2994-7"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.taml.2020.01.039"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/0041-5553(67)90144-9"},{"key":"ref28","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/BF01589116"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2019.109136"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1002\/aic.17095"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008306431147"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.03.091"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2022.114823"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10580894.pdf?arnumber=10580894","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T05:22:42Z","timestamp":1721280162000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10580894\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":35,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3422224","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}