A review of nonlinear FFT-based computational homogenization methods
M Schneider - Acta Mechanica, 2021 - Springer
Since their inception, computational homogenization methods based on the fast Fourier
transform (FFT) have grown in popularity, establishing themselves as a powerful tool …
transform (FFT) have grown in popularity, establishing themselves as a powerful tool …
A review on data-driven constitutive laws for solids
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 …
surrogate, or emulate constitutive laws that describe the path-independent and path …
Thermodynamics-based artificial neural networks for constitutive modeling
Abstract Machine Learning methods and, in particular, Artificial Neural Networks (ANNs)
have demonstrated promising capabilities in material constitutive modeling. One of the main …
have demonstrated promising capabilities in material constitutive modeling. One of the main …
FE: an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining
Herein, we present a new data-driven multiscale framework called FE ANN which is based
on two main keystones: the usage of physics-constrained artificial neural networks (ANNs) …
on two main keystones: the usage of physics-constrained artificial neural networks (ANNs) …
[HTML][HTML] Deep CNNs as universal predictors of elasticity tensors in homogenization
B Eidel - Computer Methods in Applied Mechanics and …, 2023 - Elsevier
In the present work, 3D convolutional neural networks (CNNs) are trained to link random
heterogeneous, multiphase materials to their elastic macroscale stiffness thus replacing …
heterogeneous, multiphase materials to their elastic macroscale stiffness thus replacing …
[HTML][HTML] An FE–DMN method for the multiscale analysis of short fiber reinforced plastic components
In this work, we propose a fully coupled multiscale strategy for components made from short
fiber reinforced composites, where each Gauss point of the macroscopic finite element …
fiber reinforced composites, where each Gauss point of the macroscopic finite element …
A comparative study on different neural network architectures to model inelasticity
The mathematical formulation of constitutive models to describe the path‐dependent, that is,
inelastic, behavior of materials is a challenging task and has been a focus in mechanics …
inelastic, behavior of materials is a challenging task and has been a focus in mechanics …
Microstructure-guided deep material network for rapid nonlinear material modeling and uncertainty quantification
Modeling nonlinear materials with arbitrary microstructures and loading paths is crucial in
structural analyses with heterogeneous materials with uncertainty. However, it is …
structural analyses with heterogeneous materials with uncertainty. However, it is …
[HTML][HTML] On mean field homogenization schemes for short fiber reinforced composites: unified formulation, application and benchmark
PA Hessman, F Welschinger, K Hornberger… - International Journal of …, 2021 - Elsevier
This paper revisits the topic of mean field homogenization for short fiber reinforced
composite materials. A short glass fiber reinforced thermoplastic polyamide 6.6 with a fiber …
composite materials. A short glass fiber reinforced thermoplastic polyamide 6.6 with a fiber …
Deep homogenization networks for elastic heterogeneous materials with two-and three-dimensional periodicity
We present a deep learning framework that leverages computational homogenization
expertise to predict the local stress field and homogenized moduli of heterogeneous …
expertise to predict the local stress field and homogenized moduli of heterogeneous …