107319 20240319080945.0 doi 10.1002/nme.6535 sideral 119776 ART-2022-119776 eng Moya, Beatriz Universidad de Zaragoza (orcid)0000-0001-5483-6012 Digital twins that learn and correct themselves 2022 Access copy available to the general public Unrestricted Digital twins can be defined as digital representations of physical entities that employ real‐time data to enable understanding of the operating conditions of these entities. Here we present a particular type of digital twin that involves a combination of computer vision, scientific machine learning, and augmented reality. This novel digital twin is able, therefore, to see, to interpret what it sees—and, if necessary, to correct the model it is equipped with—and presents the resulting information in the form of augmented reality. The computer vision capabilities allow the twin to receive data continuously. As any other digital twin, it is equipped with one or more models so as to assimilate data. However, if persistent deviations from the predicted values are found, the proposed methodology is able to correct on the fly the existing models, so as to accommodate them to the measured reality. Finally, the suggested methodology is completed with augmented reality capabilities so as to render a completely new type of digital twin. These concepts are tested against a proof‐of‐concept model consisting on a nonlinear, hyperelastic beam subjected to moving loads whose exact position is to be determined. info:eu-repo/grantAgreement/ES/MINECO/DPI2017-85139-C2-1-R info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 2.9 2022 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 27 / 107 = 0.252 2022 Q2 T1 ENGINEERING, MULTIDISCIPLINARY 39 / 90 = 0.433 2022 Q2 T2 1.043 2022 Applied Mathematics 2022 Q1 Numerical Analysis 2022 Q1 Engineering (miscellaneous) 2022 Q1 5.2 2022 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Badías, Alberto (orcid)0000-0001-7639-6767 Alfaro, Icíar Universidad de Zaragoza (orcid)0000-0002-9135-866X Chinesta, Francisco Cueto, Elías Universidad de Zaragoza (orcid)0000-0003-1017-4381 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 123, 13 (2022), 3034-3044 Int. j. numer. methods eng. International Journal for Numerical Methods in Engineering 0029-5981 3583335 http://zaguan.unizar.es/record/107319/files/texto_completo.pdf Postprint 1965369 http://zaguan.unizar.es/record/107319/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:107319 articulos driver 2024-03-18-12:30:47 ARTICLE