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 …

The recent decades have seen various attempts at accelerating the process of developing
materials targeted towards specific applications. The performance required for a particular …

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 the present work, two machine learning based constitutive models for finite deformations
are proposed. Using input convex neural networks, the models are hyperelastic, anisotropic …

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 …

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) …

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 …

Material modeling using modern numerical methods accelerates the design process and
reduces the costs of developing new products. However, for multiscale modeling of …

The history-dependent behaviors of classical plasticity models are often driven by internal
variables evolved according to phenomenological laws. The difficulty to interpret how these …

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 …