5 citations · 5 across the 4 of their papers we have counts for
4 papers
Bridging nanoparticle morphology and viscoelastic behavior in epoxy nanocomposites: A coarse-grained simulation-informed constitutive model
Atiyeh Hentea, Shadab Zakavatib, Behrouz Arash +2
Accurate prediction of the material behavior of polymer nanocomposites under various thermomechanical loading conditions is increasingly demanded for engineering applications. This…
A hybrid electromechanical phase-field and deep learning framework for predicting fracture in dielectric nanocomposites
Aamir Dean, Jaykumar Mavani, Betim Bahtiri +2
The accurate and efficient prediction of crack propagation in dielectric materials is a critical challenge in structural health monitoring and the design of smart systems. This wor…
A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites
Betim Bahtiri, Behrouz Arash, Sven Scheffler +2
This work proposes a physics-informed deep learning (PIDL)-based constitutive model for investigating the viscoelastic-viscoplastic behavior of short fiber-reinforced nanoparticle-…
Cyclic viscoelastic-viscoplastic behavior of epoxy nanocomposites under hygrothermal conditions: A phase-field fracture model
Behrouz Arash, Shadab Zakavati, Betim Bahtiri +2
In this study, a finite deformation phase-field formulation is developed to investigate the effect of hygrothermal conditions on the viscoelastic-viscoplastic fracture behavior of…