000089831 001__ 89831 000089831 005__ 20230914083256.0 000089831 0247_ $$2doi$$a10.1016/j.promfg.2020.04.211 000089831 0248_ $$2sideral$$a118207 000089831 037__ $$aART-2020-118207 000089831 041__ $$aeng 000089831 100__ $$0(orcid)0000-0003-3003-5856$$aGonzález, D.$$uUniversidad de Zaragoza 000089831 245__ $$aScientific machine learning for coarse-grained constitutive models 000089831 260__ $$c2020 000089831 5060_ $$aAccess copy available to the general public$$fUnrestricted 000089831 5203_ $$aWe present here a review on some of our latest works concerning the development of thermodynamics-aware machine learning strategies for the data-driven construction of constitutive models. We suggest a methodology constructed upon three main ingredients. (i) the employ of manifold learning strategies to unveil the true dimensionality of data, thus pointing out the need for the definition of “internal” variables, different of the experimental ones. (ii) the process will be described by the so-called General Equation for the Non-Equilibrium Reversible-Irreversible Coupling (GENERIC). (iii) the precise form of the GENERIC terms will be unveiled by regression of data. 000089831 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000089831 592__ $$a0.504$$b2020 000089831 593__ $$aIndustrial and Manufacturing Engineering$$c2020$$dQ2 000089831 593__ $$aArtificial Intelligence$$c2020$$dQ2 000089831 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000089831 700__ $$aChinesta, F. 000089831 700__ $$0(orcid)0000-0003-1017-4381$$aCueto, E.$$uUniversidad de Zaragoza 000089831 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est. 000089831 773__ $$g47 (2020), 693-695$$tProcedia Manufacturing$$x2351-9789 000089831 8564_ $$s182168$$uhttps://zaguan.unizar.es/record/89831/files/texto_completo.pdf$$yVersión publicada 000089831 8564_ $$s377651$$uhttps://zaguan.unizar.es/record/89831/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000089831 909CO $$ooai:zaguan.unizar.es:89831$$particulos$$pdriver 000089831 951__ $$a2023-09-13-10:51:50 000089831 980__ $$aARTICLE