Data-driven GENERIC modeling of poroviscoelastic materials
TypeArticles dans des revues avec comité de lecture
Biphasic soft materials are challenging to model by nature. Ongoing efforts are targeting their effective modeling and simulation. This work uses experimental atomic force nanoindentation of thick hydrogels to identify the indentation forces are a function of the indentation depth. Later on, the atomic force microscopy results are used in a GENERIC general equation for non-equilibrium reversible-irreversible coupling (GENERIC) formalism to identify the best model conserving basic thermodynamic laws. The data-driven GENERIC analysis identifies the material behavior with high fidelity for both data fitting and prediction.
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CHINESTA, Francisco; LEYGUE, Adrien; BORDEU, Felipe; AGUADO, Jose Vicente; CUETO, Elias; GONZALEZ, David; ALFARO, Iciar; AMMAR, Amine; HUERTA, Antonio (Springer, 2013)In this paper we are addressing a new paradigm in the field of simulation-based engineering sciences (SBES) to face the challenges posed by current ICT technologies. Despite the impressive progress attained by simulation ...
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BADIAS, Alberto; ALFARO, Iciar; GONZALEZ, David; CHINESTA, Francisco; CUETO, Elias (elsevier, 2018)In this work we explore the possibilities of reduced order modeling for augmented reality applications. We consider parametric reduced order models based upon separate (affine) parametric dependence so as to speedup the ...
MOYA, Beatriz; GONZÁLEZ, David; ALFARO, Iciar; CHINESTA, Francisco; CUETO, Elías G. (Springer, 2019)In this work we study several learning strategies for fluid sloshing problems based on data. In essence, a reduced-order model of the dynamics of the free surface motion of the fluid is developed under rigorous thermodynamics ...