119019 20221020151542.0 doi 10.1017/dce.2021.16 sideral 130232 ART-2021-130232 eng Sancarlos, Abel Learning stable reduced-order models for hybrid twins 2021 The concept of “hybrid twin” (HT) has recently received a growing interest thanks to the availability of powerful machine learning techniques. This twin concept combines physics-based models within a model order reduction framework—to obtain real-time feedback rates—and data science. Thus, the main idea of the HT is to develop on-the-fly data-driven models to correct possible deviations between measurements and physics-based model predictions. This paper is focused on the computation of stable, fast, and accurate corrections in the HT framework. Furthermore, regarding the delicate and important problem of stability, a new approach is proposed, introducing several subvariants and guaranteeing a low computational cost as well as the achievement of a stable time-integration. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/ES/MINECO-CICYT/DPI2017-85139-C2-1-R info:eu-repo/grantAgreement/ES/UZ/ESI-ENSAM-Simulated Reality info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 1.6 2021 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Cameron, Morgan Le Peuvedic, Jean-Marc Groulier, Juliette Duval, Jean-Louis Cueto, Elias Universidad de Zaragoza (orcid)0000-0003-1017-4381 Chinesta, Francisco 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 2 (2021), e10 [20 pp.] Data-Centric Engineering 2632-6736 1962835 http://zaguan.unizar.es/record/119019/files/texto_completo.pdf Versión publicada 1762273 http://zaguan.unizar.es/record/119019/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:119019 articulos driver 2022-10-20-09:19:41 ARTICLE