Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics

DOI10.1016/j.cma.2022.115497OpenAlexW4290849546MaRDI QIDQ2083129

H. Wessels, Alexander Henkes

Publication date: 10 October 2022

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2206.01693

zbMATH Keywords

artificial neural networksmicrostructure generationgenerative adversarial networksfull-field homogenization

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Micromechanics of solids (74M25)

Related Items (4)

Deep CNNs as universal predictors of elasticity tensors in homogenization ⋮ Two-stage 2D-to-3d reconstruction of realistic microstructures: implementation and numerical validation by effective properties ⋮ Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties ⋮ Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics

Uses Software

• Python
• TensorFlow
• PoreSpy
• StyleGAN
• Wasserstein GAN
• FFTHomPy

Cites Work

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• Uncertainty quantification in computational stochastic multiscale analysis of nonlinear elastic materials
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• The neural particle method - an updated Lagrangian physics informed neural network for computational fluid dynamics
• Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics
• Physics informed neural networks for continuum micromechanics
• A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures
• Finite strain FFT-based non-linear solvers made simple
• An FFT-based Galerkin method for homogenization of periodic media
• Introduction to Micromechanics and Nanomechanics
• Neural Networks and Deep Learning
• Computational Homogenization Using Convolutional Neural Networks
• Optimal Transport

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