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 …

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 …

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 …

Sparse regression and feature extraction are the cornerstones of knowledge discovery from
massive data. Their goal is to discover interpretable and predictive models that provide …

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 …

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) …

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 …