Abstract Machine learning models are increasingly used in many engineering fields thanks
to the widespread digital data, growing computing power, and advanced algorithms. The …

Abstract Machine learning models have been shown to be useful for predicting and
assessing structural performance, identifying structural condition and informing preemptive …

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 …

A mechanics‐informed artificial neural network approach for learning constitutive laws
governing complex, nonlinear, elastic materials from strain–stress data is proposed. The …

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 …

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 …

Abstract Machine Learning methods and, in particular, Artificial Neural Networks (ANNs)
have demonstrated promising capabilities in material constitutive modeling. One of the main …

Predicting physical response of an artificially structured material is of particular interest for
scientific and engineering applications. Here we use deep learning to predict optical …

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 …

We propose a new approach for unsupervised learning of hyperelastic constitutive laws with
physics-consistent deep neural networks. In contrast to supervised learning, which assumes …