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 …

Simple shear presents a local material structure–property relationship and plays an
important role in the development of material design, mechanical modeling, and …

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 …

In this paper we introduce constitutive artificial neural networks (CANNs), a novel machine
learning architecture for data-driven modeling of the mechanical constitutive behavior of …

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 …

Abstract We extend the Data-Driven formulation of problems in elasticity of Kirchdoerfer and
Ortiz (2016) to inelasticity. This extension differs fundamentally from Data-Driven problems …

Abstract We present a Data-Driven framework for multiscale mechanical analysis of
materials. The proposed framework relies on the Data-Driven formulation in mechanics …

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