Scientific machine learning for coarse-grained constitutive models
González, D. (Universidad de Zaragoza) ; Chinesta, F. ; Cueto, E. (Universidad de Zaragoza)
Resumen: 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.
Idioma: Inglés
DOI: 10.1016/j.promfg.2020.04.211
Año: 2020
Publicado en: Procedia Manufacturing 47 (2020), 693-695
ISSN: 2351-9789
Factor impacto SCIMAGO: 0.504 - Industrial and Manufacturing Engineering (Q2) - Artificial Intelligence (Q2)
Tipo y forma: Article (Published version)
Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you may not distribute the modified material.
Exportado de SIDERAL (2023-09-13-10:51:50)
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Idioma: Inglés
DOI: 10.1016/j.promfg.2020.04.211
Año: 2020
Publicado en: Procedia Manufacturing 47 (2020), 693-695
ISSN: 2351-9789
Factor impacto SCIMAGO: 0.504 - Industrial and Manufacturing Engineering (Q2) - Artificial Intelligence (Q2)
Tipo y forma: Article (Published version)
Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you may not distribute the modified material. Exportado de SIDERAL (2023-09-13-10:51:50)
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Este artículo se encuentra en las siguientes colecciones:
Articles > Artículos por área > Mec. de Medios Contínuos y Teor. de Estructuras
Record created 2020-06-25, last modified 2023-09-14