Symbolic regression (SR) is a machine learning-based regression method based on genetic
programming principles that integrates techniques and processes from heterogeneous …

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 …

Constitutive modeling based on continuum mechanics theory has been a classical approach
for modeling the mechanical responses of materials. However, when constitutive laws are …

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 …

In the framework of solid mechanics, the task of deriving material parameters from
experimental data has recently re-emerged with the progress in full-field measurement …

The fatigue life is one of the essential design indexes of the cement-stabilized cold recycled
mixtures (CSCRM) in China. However, it is challenging to determine the fatigue equation of …

We present a machine learning framework capable of consistently inferring mathematical
expressions of hyperelastic energy functionals for incompressible materials from sparse …

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

In this article, we present a new procedure to automatically generate interpretable
hyperelastic material models. This approach is based on symbolic regression which …

Nanoporous metals, built out of complex ligament networks, can be produced with an
additional level of hierarchy. The resulting complexity of the structure makes modelling of the …