37 citations · 61 across the 4 of their papers we have counts for
4 papers
Recovering Mullins damage hyperelastic behaviour with physics augmented neural networks
Martin Zlatić, Marko Čanađija
The aim of this work is to develop a neural network for modelling incompressible hyperelastic behaviour with isotropic damage, the so-called Mullins effect. This is obtained throug…
A computational framework for nanotrusses: input convex neural networks approach
Marko Čanađija, Valentina Košmerl, Martin Zlatić +2
The present research aims to provide a practical numerical tool for the mechanical analysis of nanoscale trusses with similar accuracy to molecular dynamics (MD). As a first step,…
Deep learning framework for carbon nanotubes: mechanical properties and modeling strategies
Marko Canadija
Tensile tests at room temperature are performed using molecular dynamics on all configurations of single-walled carbon nanotubes up to 4 nm in diameter. Distributions of the Young'…
Dynamic behavior of nanobeams under axial loads: Integral elasticity modeling and size-dependent eigenfrequencies assessment
Raffaele Barretta, Marko Čanađija, Francesco Marotti de Sciarra +2
In this article, eigenfrequencies of nano-beams under axial loads are assessed by making recourse to the well-posed stress-driven nonlocal model (SDM) and strain-driven two-phase l…