With an emphasis on the development of machine learning-based constitutive modeling
approaches, the state of constitutive modeling techniques and applications for metals and …

Calculation of transient temperature fields is widely used in engineering application and is
also very crucial. Nevertheless, the existing methods for the prediction of complex transient …

This work presents a physics-informed neural network (PINN) based framework to model the
strain-rate and temperature dependence of the deformation fields in elastic-viscoplastic …

In the present work, a novel modeling strategy to accelerate multi-scale simulations of
heterogeneous materials using deep neural networks is developed. This approach, called …

Rapid reliability analysis of the solder joints in the electronic devices identifies as an
appeared gap in the design stage. This paper demonstrates the feasibility of employing the …

In this work, we propose a deep learning (DL)-based constitutive model for investigating the
cyclic viscoelastic-viscoplastic-damage behavior of nanoparticle/epoxy nanocomposites …

Additive manufacturing facilitates the production of parts with tailored mechanical properties,
yet achieving specific stress–strain responses remains a significant challenge due to the …

The thermo-mechanical fatigue of different SAC+ solders is investigated using transient
thermal analysis (TTA) and predicted using artificial neural networks (ANN). TTA measures …

Modeling systems from collected data faces two main difficulties: the first one concerns the
choice of measurable variables that will define the learnt model features, which should be …

The high-temperature compression characteristics of a Ti-55511 alloy are explored through
adopting two-stage high-temperature compressed experiments with step-like strain rates …