Now showing items 1-3 of 3

    • A data-driven learning method for constitutive modeling: application to vascular hyperelastic soft tissues 

      González Ibáñez, David; García González, Alberto; Chinesta Soria, Francisco; Cueto Prendes, Elias (Multidisciplinary Digital Publishing Institute (MDPI), 2020-05-01)
      Article
      Open Access
      We address the problem of machine learning of constitutive laws when large experimental deviations are present. This is particularly important in soft living tissue modeling, for instance, where large patient-dependent ...
    • A multidimensional data-driven sparse identification technique: the sparse proper generalized decomposition 

      Ibáñez Pinillo, Rubén; Abisset Chavanne, Emmanuelle; Ammar, Amine; González Ibáñez, David; Cueto Prendes, Elias; Huerta, Antonio; Duval, Jean Louis; Chinesta Soria, Francisco (2018-01-01)
      Article
      Open Access
      Sparse model identification by means of data is especially cumbersome if the sought dynamics live in a high dimensional space. This usually involves the need for large amount of data, unfeasible in such a high dimensional ...
    • Vademecum-based GFEM (V-GFEM): optimal enrichment for transient problems 

      Canales, Diego; Leygue, Adrien; Chinesta Soria, Francisco; González Ibáñez, David; Cueto Prendes, Elias; Feulvarch, Eric; Bergheau, Jean-Michel; Huerta, Antonio (2016-11)
      Article
      Open Access
      This paper proposes a generalized finite element method based on the use of parametric solutions as enrichment functions. These parametric solutions are precomputed off-line and stored in memory in the form of a computational ...