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000089820 0247_ $$2doi$$a10.3390/ma13102319
000089820 0248_ $$2sideral$$a118173
000089820 037__ $$aART-2020-118173
000089820 041__ $$aeng
000089820 100__ $$0(orcid)0000-0003-3003-5856$$aGonzález, D.$$uUniversidad de Zaragoza
000089820 245__ $$aA data-driven learning method for constitutive modeling: Application to vascular hyperelastic soft tissues
000089820 260__ $$c2020
000089820 5060_ $$aAccess copy available to the general public$$fUnrestricted
000089820 5203_ $$aWe address the problem of machine learning of constitutive laws when large experimental deviations are present. This is particularly important in soft living tissue modeling, for instance, where large patient-dependent data is found. We focus on two aspects that complicate the problem, namely, the presence of an important dispersion in the experimental results and the need for a rigorous compliance to thermodynamic settings. To address these difficulties, we propose to use, respectively, Topological Data Analysis techniques and a regression over the so-called General Equation for the Nonequilibrium Reversible-Irreversible Coupling (GENERIC) formalism (M. Grmela and H. Ch. Oettinger, Dynamics and thermodynamics of complex fluids. I. Development of a general formalism. Phys. Rev. E 56, 6620, 1997). This allows us, on one hand, to unveil the true "shape" of the data and, on the other, to guarantee the fulfillment of basic principles such as the conservation of energy and the production of entropy as a consequence of viscous dissipation. Examples are provided over pseudo-experimental and experimental data that demonstrate the feasibility of the proposed approach.
000089820 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/T24-20R$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2017-85139-C2-1-R
000089820 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000089820 590__ $$a3.623$$b2020
000089820 591__ $$aMETALLURGY & METALLURGICAL ENGINEERING$$b17 / 80 = 0.213$$c2020$$dQ1$$eT1
000089820 591__ $$aMATERIALS SCIENCE, MULTIDISCIPLINARY$$b152 / 333 = 0.456$$c2020$$dQ2$$eT2
000089820 591__ $$aPHYSICS, CONDENSED MATTER$$b27 / 69 = 0.391$$c2020$$dQ2$$eT2
000089820 591__ $$aPHYSICS, APPLIED$$b51 / 160 = 0.319$$c2020$$dQ2$$eT1
000089820 591__ $$aCHEMISTRY, PHYSICAL$$b79 / 162 = 0.488$$c2020$$dQ2$$eT2
000089820 592__ $$a0.682$$b2020
000089820 593__ $$aMaterials Science (miscellaneous)$$c2020$$dQ2
000089820 593__ $$aCondensed Matter Physics$$c2020$$dQ2
000089820 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000089820 700__ $$aGarcía-González, A.
000089820 700__ $$aChinesta, F.
000089820 700__ $$0(orcid)0000-0003-1017-4381$$aCueto, E.$$uUniversidad de Zaragoza
000089820 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000089820 773__ $$g13, 10 (2020), 2319 [17 pp]$$pMATERIALS$$tMATERIALS$$x1996-1944
000089820 8564_ $$s329783$$uhttps://zaguan.unizar.es/record/89820/files/texto_completo.pdf$$yVersión publicada
000089820 8564_ $$s483852$$uhttps://zaguan.unizar.es/record/89820/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000089820 909CO $$ooai:zaguan.unizar.es:89820$$particulos$$pdriver
000089820 951__ $$a2021-09-02-10:02:18
000089820 980__ $$aARTICLE