In the framework of solid mechanics, the task of deriving material parameters from
experimental data has recently re-emerged with the progress in full-field measurement …

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 …

The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …

The brain is our softest and most vulnerable organ, and understanding its physics is a
challenging but significant task. Throughout the past decade, numerous competing models …

Misfolded tau proteins play a critical role in the progression and pathology of Alzheimer's
disease. Recent studies suggest that the spatio-temporal pattern of misfolded tau follows a …

Constitutive modeling is the cornerstone of computational and structural mechanics. In a
finite element analysis, the constitutive model is encoded in the material subroutine, a …

Data-driven methods have changed the way we understand and model materials. However,
while providing unmatched flexibility, these methods have limitations such as reduced …

Choosing the best constitutive model and the right set of model parameters is at the heart of
continuum mechanics. For decades, the gold standard in constitutive modeling has been to …

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 …