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 …

The mathematical description of elastoplasticity is a highly complex problem due to the
possible change from elastic to elasto-plastic behavior (and vice-versa) as a function of the …

The use of micromechanics in conjunction with homogenization theory allows for the
prediction of the effective mechanical properties of materials based on microstructural …

Abstract Recurrent Neural Network (RNN) based surrogate models constitute an emerging
class of reduced order models of history-dependent material behavior. Recently, the authors …

Constitutive relation remains one of the most important, yet fundamental challenges in the
study of granular materials. Instead of using closed-form phenomenological models or …

Recurrent neural networks could serve as surrogate material models, removing the gap
between component-level finite element simulations and numerically costly microscale …

Neural networks are universal function approximators that form the backbone of most
modern machine learning based models. Starting from a conventional return-mapping …

Both additively-manufactured and cast metals are known to exhibit stochastic mechanical
properties at the macroscopic level. Using a robot-assisted mechanical testing system, more …

Constitutive models dealing with the thermal and visco-plasticity of metals have seen wide
applications in the automotive industry. A basic plasticity and fracture characterization of a …