74852 20191122145056.0 doi 10.1016/j.cma.2017.08.027 sideral 101854 ART-2018-101854 eng Ayensa-Jiménez, Jacobo A new reliability-based data-driven approach for noisy experimental data with physical constraints 2018 Access copy available to the general public Unrestricted Data 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. info:eu-repo/grantAgreement/ES/MINECO/MAT2016-76039-C4-4-R info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 4.821 2018 MECHANICS 6 / 134 = 0.045 2018 Q1 T1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 2 / 105 = 0.019 2018 Q1 T1 ENGINEERING, MULTIDISCIPLINARY 6 / 88 = 0.068 2018 Q1 T1 2.996 2018 Computational Mechanics 2018 Q1 Computer Science Applications 2018 Q1 Physics and Astronomy (miscellaneous) 2018 Q1 Mechanics of Materials 2018 Q1 Mechanical Engineering 2018 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion (orcid)0000-0003-0088-7222 Doweidar, Mohamed H. Universidad de Zaragoza Sanz-Herrera, Jose A. (orcid)0000-0001-8741-6452 Doblaré, Manuel Universidad de Zaragoza 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 328 (2018), 752-774 Comput. methods appl. mech. eng. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 0045-7825 2749621 http://zaguan.unizar.es/record/74852/files/texto_completo.pdf Postprint 57879 http://zaguan.unizar.es/record/74852/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:74852 articulos driver 2019-11-22-14:46:23 ARTICLE