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 …

Full‐field optical measurements like digital image correlation or the grid method have
brought a paradigm shift in the experimental mechanics community. While inverse …

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 …

We propose a new approach for data-driven automated discovery of isotropic hyperelastic
constitutive laws. The approach is unsupervised, ie, it requires no stress data but only …

We propose an approach for data-driven automated discovery of material laws, which we
call EUCLID (Efficient Unsupervised Constitutive Law Identification and Discovery), and we …

Within the scope of our recent approach for Efficient Unsupervised Constitutive Law
Identification and Discovery (EUCLID), we propose an unsupervised Bayesian learning …

Herein, an artificial neural network (ANN)-based approach for the efficient automated
modeling and simulation of isotropic hyperelastic solids is presented. Starting from a large …

With the advent of camera‐based full‐field measurement techniques such as digital image
correlation (DIC), researchers have been trying to exploit such rich data sets through the use …

A nominal finite element solver is proposed for data-driven finite strain elasticity. It bypasses
the need for a constitutive model by considering a database of deformation gradient/first …