The numerical simulation of sheet metal forming processes depends on the accuracy of the
constitutive model used to represent the mechanical behaviour of the materials. The …

The formulation of history-dependent material laws has been a significant research and
industrial activity in solid mechanics for over a century. A large variety of models has been …

Experimental solid mechanics is at a pivotal point where machine learning (ML) approaches
are rapidly proliferating into the discovery process due to significant advances in data …

The development of highly accurate constitutive models for materials that undergo path-
dependent processes continues to be a complex challenge in computational solid …

The history-dependent behaviors of classical plasticity models are often driven by internal
variables evolved according to phenomenological laws. The difficulty to interpret how these …

Data-driven constitutive modeling with neural networks has received increased interest in
recent years due to its ability to easily incorporate physical and mechanistic constraints and …

Kirigami-inspired designs hold great potential for the development of functional materials
and devices, but predicting the morphological configuration of these structures under …

This study proposes a machine learning-based constitutive model for anisotropic plasticity in
sheet metals. A fully connected deep neural network (DNN) is constructed to learn the stress …

In this study, we investigate the impact of pore distribution and size on the mechanical
response of elastic porous materials. Two-dimensional porous media with convex porosity …

Data-driven models utilizing powerful artificial intelligence (AI) algorithms have been
implemented over the past two decades in different fields of simulation-based engineering …