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)