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 numerical simulation of sheet metal forming processes depends on the accuracy of the
constitutive model used to represent the mechanical behaviour of the materials. The …

We introduce a deep learning framework designed to train smoothed elastoplasticity models
with interpretable components, such as the stored elastic energy function, yield surface, and …

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 …

The analytical description of path-dependent elastic-plastic responses of a granular system
is highly complicated because of continuously evolving microstructures and strain …

Data-driven approaches promise to usher in a new phase of development in fracture
mechanics, but very little is currently known about how data-driven knowledge extraction …

For its robust predictive power (compared to pure physics-based models) and sample-
efficient training (compared to pure deep learning models), physics-informed deep learning …

Conventional neural network elastoplasticity models are often perceived as lacking
interpretability. This paper introduces a two-step machine learning approach that returns …

This paper introduces a neural kernel method to generate machine learning plasticity
models for micropolar and micromorphic materials that lack material symmetry and have …

Analyzing and modeling the constitutive behavior of materials is a core area in materials
sciences and a prerequisite for conducting numerical simulations in which the material …