A new reliability-based data-driven approach for noisy experimental data with physical constraints
Resumen: 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.
Idioma: Inglés
DOI: 10.1016/j.cma.2017.08.027
Año: 2018
Publicado en: COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 328 (2018), 752-774
ISSN: 0045-7825

Factor impacto JCR: 4.821 (2018)
Categ. JCR: MECHANICS rank: 6 / 134 = 0.045 (2018) - Q1 - T1
Categ. JCR: MATHEMATICS, INTERDISCIPLINARY APPLICATIONS rank: 2 / 105 = 0.019 (2018) - Q1 - T1
Categ. JCR: ENGINEERING, MULTIDISCIPLINARY rank: 6 / 88 = 0.068 (2018) - Q1 - T1

Factor impacto SCIMAGO: 2.996 - Computational Mechanics (Q1) - Computer Science Applications (Q1) - Physics and Astronomy (miscellaneous) (Q1) - Mechanics of Materials (Q1) - Mechanical Engineering (Q1)

Financiación: info:eu-repo/grantAgreement/ES/MINECO/MAT2016-76039-C4-4-R
Tipo y forma: Artículo (PostPrint)
Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)

Creative Commons Debe reconocer adecuadamente la autoría, proporcionar un enlace a la licencia e indicar si se han realizado cambios. Puede hacerlo de cualquier manera razonable, pero no de una manera que sugiera que tiene el apoyo del licenciador o lo recibe por el uso que hace. No puede utilizar el material para una finalidad comercial. Si remezcla, transforma o crea a partir del material, no puede difundir el material modificado.


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