000126282 001__ 126282
000126282 005__ 20240731103400.0
000126282 0247_ $$2doi$$a10.1186/s40323-023-00240-4
000126282 0248_ $$2sideral$$a133603
000126282 037__ $$aART-2023-133603
000126282 041__ $$aeng
000126282 100__ $$aSancarlos, Abel
000126282 245__ $$aRegularized regressions for parametric models based on separated representations
000126282 260__ $$c2023
000126282 5060_ $$aAccess copy available to the general public$$fUnrestricted
000126282 5203_ $$aRegressions 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.
000126282 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/T24-20R$$9info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2020-113463RB-C31/AEI/10.13039/501100011033
000126282 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000126282 592__ $$a0.741$$b2023
000126282 593__ $$aEngineering (miscellaneous)$$c2023$$dQ1
000126282 593__ $$aModeling and Simulation$$c2023$$dQ2
000126282 593__ $$aApplied Mathematics$$c2023$$dQ2
000126282 593__ $$aComputer Science Applications$$c2023$$dQ2
000126282 594__ $$a6.8$$b2023
000126282 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000126282 700__ $$aChampaney, Víctor
000126282 700__ $$0(orcid)0000-0003-1017-4381$$aCueto, Elías$$uUniversidad de Zaragoza
000126282 700__ $$aChinesta, Francisco
000126282 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000126282 773__ $$g10 (2023), 4 [26 pp.]$$pAdv. model. simul. eng. sci.$$tAdvanced modeling and simulation in engineering sciences$$x2213-7467
000126282 8564_ $$s3271029$$uhttps://zaguan.unizar.es/record/126282/files/texto_completo.pdf$$yVersión publicada
000126282 8564_ $$s2215292$$uhttps://zaguan.unizar.es/record/126282/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000126282 909CO $$ooai:zaguan.unizar.es:126282$$particulos$$pdriver
000126282 951__ $$a2024-07-31-09:58:43
000126282 980__ $$aARTICLE