This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …

For more than 100 years, chemical, physical, and material scientists have proposed
competing constitutive models to best characterize the behavior of natural and man-made …

In the present work, a hyperelastic constitutive model based on neural networks is proposed
which fulfills all common constitutive conditions by construction, and in particular, is …

We extend the scope of our recently developed approach for unsupervised automated
discovery of material laws (denoted as EUCLID) to the general case of a material belonging …

Herein, we present a new data-driven multiscale framework called FE ANN which is based
on two main keystones: the usage of physics-constrained artificial neural networks (ANNs) …

The complex magneto-mechanical coupling that governs the material response of
magnetorheological elastomers (MREs) requires computational tools to assist the design …

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

Spiking neural networks (SNN), also often referred to as the third generation of neural
networks, carry the potential for a massive reduction in memory and energy consumption …

The stiffness of soft biological tissues not only depends on the applied deformation, but also
on the deformation rate. To model this type of behavior, traditional approaches select a …

We present a framework for the multiscale modeling of finite strain magneto-elasticity based
on physics-augmented neural networks (NNs). By using a set of problem specific invariants …