000078219 001__ 78219 000078219 005__ 20200716101529.0 000078219 0247_ $$2doi$$a10.3389/fmats.2019.00014 000078219 0248_ $$2sideral$$a110748 000078219 037__ $$aART-2019-110748 000078219 041__ $$aeng 000078219 100__ $$0(orcid)0000-0003-3003-5856$$aGonzalez, D.$$uUniversidad de Zaragoza 000078219 245__ $$aLearning Corrections for Hyperelastic Models From Data 000078219 260__ $$c2019 000078219 5060_ $$aAccess copy available to the general public$$fUnrestricted 000078219 5203_ $$aUnveiling physical laws from data is seen as the ultimate sign of human intelligence. While there is a growing interest in this sense around the machine learning community, some recent works have attempted to simply substitute physical laws by data. We believe that getting rid of centuries of scientific knowledge is simply nonsense. There are models whose validity and usefulness is out of any doubt, so try to substitute them by data seems to be a waste of knowledge. While it is true that fitting well-known physical laws to experimental data is sometimes a painful process, a good theory continues to be practical and provide useful insights to interpret the phenomena taking place. That is why we present here a method to construct, based on data, automatic corrections to existing models. Emphasis is put in the correct thermodynamic character of these corrections, so as to avoid violations of first principles such as the laws of thermodynamics. These corrections are sought under the umbrella of the GENERIC framework (Grmela and Oettinger, 1997), a generalization of Hamiltonian mechanics to non-equilibrium thermodynamics. This framework ensures the satisfaction of the first and second laws of thermodynamics, while providing a very appealing context for the proposed automated correction of existing laws. In this work we focus on solid mechanics, particularly large strain (visco-)hyperelasticity. 000078219 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/T24-17R$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2017-85139-C2-1-R 000078219 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000078219 590__ $$a2.705$$b2019 000078219 591__ $$aMATERIALS SCIENCE, MULTIDISCIPLINARY$$b150 / 314 = 0.478$$c2019$$dQ2$$eT2 000078219 592__ $$a0.71$$b2019 000078219 593__ $$aMaterials Science (miscellaneous)$$c2019$$dQ2 000078219 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000078219 700__ $$aChinesta, F. 000078219 700__ $$0(orcid)0000-0003-1017-4381$$aCueto, E.$$uUniversidad de Zaragoza 000078219 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est. 000078219 773__ $$g6 (2019), 14 [12 pp]$$pFront. mater.$$tFrontiers in Materials$$x2296-8016 000078219 8564_ $$s275818$$uhttps://zaguan.unizar.es/record/78219/files/texto_completo.pdf$$yVersión publicada 000078219 8564_ $$s11315$$uhttps://zaguan.unizar.es/record/78219/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000078219 909CO $$ooai:zaguan.unizar.es:78219$$particulos$$pdriver 000078219 951__ $$a2020-07-16-09:32:50 000078219 980__ $$aARTICLE