126282 20240731103400.0 doi 10.1186/s40323-023-00240-4 sideral 133603 ART-2023-133603 eng Sancarlos, Abel Regularized regressions for parametric models based on separated representations 2023 Access copy available to the general public Unrestricted Regressions created from experimental or simulated data enable the construction of metamodels, widely used in a variety of engineering applications. Many engineering problems involve multi-parametric physics whose corresponding multi-parametric solutions can be viewed as a sort of computational vademecum that, once computed offline, can be then used in a variety of real-time engineering applications including optimization, inverse analysis, uncertainty propagation or simulation based control. Sometimes, these multi-parametric problems can be solved by using advanced model order reduction—MOR-techniques. However, solving these multi-parametric problems can be very costly. In that case, one possibility consists in solving the problem for a sample of the parametric values and creating a regression from all the computed solutions. The solution for any choice of the parameters is then inferred from the prediction of the regression model. However, addressing high-dimensionality at the low data limit, ensuring accuracy and avoiding overfitting constitutes a difficult challenge. The present paper aims at proposing and discussing different advanced regressions based on the proper generalized decomposition (PGD) enabling the just referred features. In particular, new PGD strategies are developed adding different regularizations to the s-PGD method. In addition, the ANOVA-based PGD is proposed to ally them. info:eu-repo/grantAgreement/ES/DGA-FSE/T24-20R info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2020-113463RB-C31/AEI/10.13039/501100011033 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 0.741 2023 Engineering (miscellaneous) 2023 Q1 Modeling and Simulation 2023 Q2 Applied Mathematics 2023 Q2 Computer Science Applications 2023 Q2 6.8 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Champaney, Víctor Cueto, Elías 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. 10 (2023), 4 [26 pp.] Adv. model. simul. eng. sci. Advanced modeling and simulation in engineering sciences 2213-7467 3271029 http://zaguan.unizar.es/record/126282/files/texto_completo.pdf Versión publicada 2215292 http://zaguan.unizar.es/record/126282/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:126282 articulos driver 2024-07-31-09:58:43 ARTICLE