Renewable energy remains a prominent research hotspot because of its potential to reduce
the global reliance on fossil fuels, which is experiencing a significant decline. Additionally, it …

In this article, a general framework is devised for reliable predictions over the local
behaviors, such as the failure strength and stress intensity factor, of multiscale configurations …

We introduce a fully Lagrangian heterogeneous multiscale method (LHMM) to model
complex fluids with microscopic features that can extend over large spatio/temporal scales …

Plastic deformation of metals involves the complex evolution of dislocations forming strongly
connected dislocation networks. These dislocation networks are based on dislocation …

We report a significantly improved accuracy in grain boundary segmentation using
Convolutional Neural Networks (CNN) trained on a combination of real and generated data …

This study presents a generalized multimesh nonlocal finite element method (M 2-FEM) that
addresses several long-standing challenges in the numerical simulation of Eringen's …

Materials with thickness ranging from a few nanometers to a single atomic layer present
unprecedented opportunities to investigate new phases of matter constrained to the two …

We implement an approach using Bayesian inference and machine learning to calibrate the
material parameters of a constitutive model for the superelastic deformation of NiTi shape …

The article describes some problems arising in the construction of models of synthesis of
composites in modern technologies, which allow predicting the evolution of composition and …

One of the major goals of computational materials science is the rapid and accurate
prediction of properties of new materials. In order to design new materials and compositions …