000074852 001__ 74852
000074852 005__ 20191122145056.0
000074852 0247_ $$2doi$$a10.1016/j.cma.2017.08.027
000074852 0248_ $$2sideral$$a101854
000074852 037__ $$aART-2018-101854
000074852 041__ $$aeng
000074852 100__ $$aAyensa-Jiménez, Jacobo
000074852 245__ $$aA new reliability-based data-driven approach for noisy experimental data with physical constraints
000074852 260__ $$c2018
000074852 5060_ $$aAccess copy available to the general public$$fUnrestricted
000074852 5203_ $$aData Science has burst into simulation-based engineering sciences with an impressive impulse. However, data are never uncertainty-free and a suitable approach is needed to face data measurement errors and their intrinsic randomness in problems with well-established physical constraints. As in previous works, this problem is here faced by hybridizing a standard mathematical modeling approach with a new data-driven solver accounting for the phenomenological part of the problem, with the aim of finding a solution point, satisfying some constraints, that minimizes a distance to a given data-set. However, unlike such works that are established in a deterministic framework, we use the Mahalanobis distance in order to incorporate statistical second order uncertainty of data in computations, i.e. variance and correlation. We develop the underlying stochastic theoretical framework and establish the fundamental mathematical and statistical properties. The performance of the resulting reliability-based data-driven procedure is evaluated in a simple but illustrative unidimensional problem as well as in a more realistic solution of a 3D structural problem with a material with intrinsically random constitutive behavior as concrete. The results show, in comparison with other data-driven solvers, better convergence, higher accuracy, clearer interpretation, and major flexibility besides the relevance of allowing uncertainty management with low computational demand.
000074852 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/MAT2016-76039-C4-4-R
000074852 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000074852 590__ $$a4.821$$b2018
000074852 591__ $$aMECHANICS$$b6 / 134 = 0.045$$c2018$$dQ1$$eT1
000074852 591__ $$aMATHEMATICS, INTERDISCIPLINARY APPLICATIONS$$b2 / 105 = 0.019$$c2018$$dQ1$$eT1
000074852 591__ $$aENGINEERING, MULTIDISCIPLINARY$$b6 / 88 = 0.068$$c2018$$dQ1$$eT1
000074852 592__ $$a2.996$$b2018
000074852 593__ $$aComputational Mechanics$$c2018$$dQ1
000074852 593__ $$aComputer Science Applications$$c2018$$dQ1
000074852 593__ $$aPhysics and Astronomy (miscellaneous)$$c2018$$dQ1
000074852 593__ $$aMechanics of Materials$$c2018$$dQ1
000074852 593__ $$aMechanical Engineering$$c2018$$dQ1
000074852 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000074852 700__ $$0(orcid)0000-0003-0088-7222$$aDoweidar, Mohamed H.$$uUniversidad de Zaragoza
000074852 700__ $$aSanz-Herrera, Jose A.
000074852 700__ $$0(orcid)0000-0001-8741-6452$$aDoblaré, Manuel$$uUniversidad de Zaragoza
000074852 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000074852 773__ $$g328 (2018), 752-774$$pComput. methods appl. mech. eng.$$tCOMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING$$x0045-7825
000074852 8564_ $$s2749621$$uhttps://zaguan.unizar.es/record/74852/files/texto_completo.pdf$$yPostprint
000074852 8564_ $$s57879$$uhttps://zaguan.unizar.es/record/74852/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000074852 909CO $$ooai:zaguan.unizar.es:74852$$particulos$$pdriver
000074852 951__ $$a2019-11-22-14:46:23
000074852 980__ $$aARTICLE