89831 20230914083256.0 doi 10.1016/j.promfg.2020.04.211 sideral 118207 ART-2020-118207 eng González, D. Universidad de Zaragoza (orcid)0000-0003-3003-5856 Scientific machine learning for coarse-grained constitutive models 2020 Access copy available to the general public Unrestricted We 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. info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 0.504 2020 Industrial and Manufacturing Engineering 2020 Q2 Artificial Intelligence 2020 Q2 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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. 47 (2020), 693-695 Procedia Manufacturing 2351-9789 182168 http://zaguan.unizar.es/record/89831/files/texto_completo.pdf Versión publicada 377651 http://zaguan.unizar.es/record/89831/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:89831 articulos driver 2023-09-13-10:51:50 ARTICLE