Next Article in Journal
Slope Entropy: A New Time Series Complexity Estimator Based on Both Symbolic Patterns and Amplitude Information
Next Article in Special Issue
Finite Amplitude Stability of Internal Steady Flows of the Giesekus Viscoelastic Rate-Type Fluid
Previous Article in Journal
Detection of Common Causes between Air Traffic Serious and Major Incidents in Applying the Convolution Operator to Heinrich Pyramid Theory
Previous Article in Special Issue
Lifts of Symmetric Tensors: Fluids, Plasma, and Grad Hierarchy
Open AccessArticle

Data-Driven GENERIC Modeling of Poroviscoelastic Materials

Mechanical Engineering Department, Notre Dame University-Louaizé, Zouk Mosbeh P.O. Box 72, Lebanon
Aragon Institute of Engineering Research, Universidad de Zaragoza, Edificio Betancourt, Maria de Luna, s.n., 50018 Zaragoza, Spain
ESI Chair @ ENSAM Arts et Metiers Institute of Technology, 151 Boulevard de l’Hôpital, F-75013 Paris, France
Author to whom correspondence should be addressed.
Entropy 2019, 21(12), 1165;
Received: 22 October 2019 / Revised: 26 November 2019 / Accepted: 26 November 2019 / Published: 28 November 2019
(This article belongs to the Special Issue Entropies: Between Information Geometry and Kinetics)

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. View Full-Text
Keywords: soft materials; biphasic materials; hydrogel; data-driven; GENERIC; modeling soft materials; biphasic materials; hydrogel; data-driven; GENERIC; modeling
Show Figures

Figure 1

MDPI and ACS Style

Ghnatios, C.; Alfaro, I.; González, D.; Chinesta, F.; Cueto, E. Data-Driven GENERIC Modeling of Poroviscoelastic Materials. Entropy 2019, 21, 1165.

AMA Style

Ghnatios C, Alfaro I, González D, Chinesta F, Cueto E. Data-Driven GENERIC Modeling of Poroviscoelastic Materials. Entropy. 2019; 21(12):1165.

Chicago/Turabian Style

Ghnatios, Chady; Alfaro, Iciar; González, David; Chinesta, Francisco; Cueto, Elias. 2019. "Data-Driven GENERIC Modeling of Poroviscoelastic Materials" Entropy 21, no. 12: 1165.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop