000107319 001__ 107319 000107319 005__ 20240319080945.0 000107319 0247_ $$2doi$$a10.1002/nme.6535 000107319 0248_ $$2sideral$$a119776 000107319 037__ $$aART-2022-119776 000107319 041__ $$aeng 000107319 100__ $$0(orcid)0000-0001-5483-6012$$aMoya, Beatriz$$uUniversidad de Zaragoza 000107319 245__ $$aDigital twins that learn and correct themselves 000107319 260__ $$c2022 000107319 5060_ $$aAccess copy available to the general public$$fUnrestricted 000107319 5203_ $$aDigital 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. 000107319 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/DPI2017-85139-C2-1-R 000107319 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/ 000107319 590__ $$a2.9$$b2022 000107319 591__ $$aMATHEMATICS, INTERDISCIPLINARY APPLICATIONS$$b27 / 107 = 0.252$$c2022$$dQ2$$eT1 000107319 591__ $$aENGINEERING, MULTIDISCIPLINARY$$b39 / 90 = 0.433$$c2022$$dQ2$$eT2 000107319 594__ $$a5.2$$b2022 000107319 592__ $$a1.043$$b2022 000107319 593__ $$aApplied Mathematics$$c2022$$dQ1 000107319 593__ $$aNumerical Analysis$$c2022$$dQ1 000107319 593__ $$aEngineering (miscellaneous)$$c2022$$dQ1 000107319 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000107319 700__ $$0(orcid)0000-0001-7639-6767$$aBadías, Alberto 000107319 700__ $$0(orcid)0000-0002-9135-866X$$aAlfaro, Icíar$$uUniversidad de Zaragoza 000107319 700__ $$aChinesta, Francisco 000107319 700__ $$0(orcid)0000-0003-1017-4381$$aCueto, Elías$$uUniversidad de Zaragoza 000107319 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est. 000107319 773__ $$g123, 13 (2022), 3034-3044$$pInt. j. numer. methods eng.$$tInternational Journal for Numerical Methods in Engineering$$x0029-5981 000107319 8564_ $$s3583335$$uhttps://zaguan.unizar.es/record/107319/files/texto_completo.pdf$$yPostprint 000107319 8564_ $$s1965369$$uhttps://zaguan.unizar.es/record/107319/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000107319 909CO $$ooai:zaguan.unizar.es:107319$$particulos$$pdriver 000107319 951__ $$a2024-03-18-12:30:47 000107319 980__ $$aARTICLE