The solution of instrumented indentation inverse problems by physically-based models still
represents a complex challenge yet to be solved in metallurgy and materials science. In …

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

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

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 …

With an emphasis on the development of machine learning-based constitutive modeling
approaches, the state of constitutive modeling techniques and applications for metals and …

The accurate description of the strain rate and temperature dependent response of metals is
a perpetual quest in crashworthiness and forming applications. In the present study …

Complex components made of 5182-O aluminum alloy are usually formed at high
temperatures due to their low ductility at room temperature, and the advanced current …

This study proposes a machine learning-based constitutive model for anisotropic plasticity in
sheet metals. A fully connected deep neural network (DNN) is constructed to learn the stress …

The Chaboche isotropic-kinematic hardening (CIKH) model provides a versatile and realistic
description of the material stress–strain behavior under generic multiaxial cyclic loadings …

Mechanics‐specific recurrent neural network (RNN) models are known for their ability to
describe the complex three‐dimensional stress–strain response of elasto‐plastic solids for …