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 …

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

This paper introduces a publicly available PyTorch-ABAQUS deep-learning framework of a
family of plasticity models where the yield surface is implicitly represented by a scalar …

We present a comparison between two approaches to modelling hyperelastic material
behaviour using data. The first approach is a novel approach based on Data-driven …

Conventional finite element methods are known to be tedious in adaptive refinements due to
their conformal regularity requirements. Further, the enrichment functions for adaptive …

Building surrogate models with uncertainty quantification capabilities is essential for many
engineering applications where randomness, such as variability in material properties, is …

In deep geological repositories for high level nuclear waste with close canister spacings,
bentonite buffers can experience temperatures higher than 100° C. In this range of extreme …

In structural analysis, the mechanical response of a material is usually approximated with a
constitutive model, ie a mathematical relation between strains and stresses. This …