89820 20210902121812.0 doi 10.3390/ma13102319 sideral 118173 ART-2020-118173 eng González, D. Universidad de Zaragoza (orcid)0000-0003-3003-5856 A data-driven learning method for constitutive modeling: Application to vascular hyperelastic soft tissues 2020 Access copy available to the general public Unrestricted We 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. info:eu-repo/grantAgreement/ES/DGA-FSE/T24-20R info:eu-repo/grantAgreement/ES/MINECO/DPI2017-85139-C2-1-R info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 3.623 2020 METALLURGY & METALLURGICAL ENGINEERING 17 / 80 = 0.213 2020 Q1 T1 MATERIALS SCIENCE, MULTIDISCIPLINARY 152 / 333 = 0.456 2020 Q2 T2 PHYSICS, CONDENSED MATTER 27 / 69 = 0.391 2020 Q2 T2 PHYSICS, APPLIED 51 / 160 = 0.319 2020 Q2 T1 CHEMISTRY, PHYSICAL 79 / 162 = 0.488 2020 Q2 T2 0.682 2020 Materials Science (miscellaneous) 2020 Q2 Condensed Matter Physics 2020 Q2 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion García-González, A. Chinesta, F. Cueto, E. Universidad de Zaragoza (orcid)0000-0003-1017-4381 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 13, 10 (2020), 2319 [17 pp] MATERIALS MATERIALS 1996-1944 329783 http://zaguan.unizar.es/record/89820/files/texto_completo.pdf Versión publicada 483852 http://zaguan.unizar.es/record/89820/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:89820 articulos driver 2021-09-02-10:02:18 ARTICLE