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Physics-Informed Machine Learning for Hybrid Digital Twin–Enhanced Damage Detection and Localization

dc.contributor.authorWang, Zixin
dc.contributor.authorJahanshahi, Mohammad R.
dc.contributor.authorLund, Alana
dc.contributor.authorShahriar, Adnan
dc.contributor.authorMontoya, Arturo
dc.date.accessioned2026-08-20T10:49:10Z
dc.date.available2026-08-20T10:49:10Z
dc.date.copyright2025/10/07
dc.date.issued2025
dc.identifier.otherJENMDT.EMENG-8325.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311302
dc.description.abstractAbstractIn structural health monitoring (SHM), structural damage detection and localization have made significant advances with deep learning–based methods. While finite element model (FEMs) have been employed to simulate a variety of damage scenarios for ...
dc.publisherAmerican Society of Civil Engineers
dc.titlePhysics-Informed Machine Learning for Hybrid Digital Twin–Enhanced Damage Detection and Localization
dc.typeJournal Article
dc.journal.volume151
dc.journal.issue12
dc.journal.titleJournal of Engineering Mechanics
dc.identifier.doi10.1061/JENMDT.EMENG-8325
dc.journal.fristpage04025080-1
dc.journal.lastpage04025080-19
dc.page19
dc.treeJournal of Engineering Mechanics:;2025:;Volume ( 151 ):;issue: 012
dc.contenttypeFulltext


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