A review of generative adversarial networks (GANs) and its applications in a wide variety of disciplines: from medical to remote sensing

A Dash, J Ye, G Wang - IEEE Access, 2023 - ieeexplore.ieee.org
We look into Generative Adversarial Network (GAN), its prevalent variants and applications
in a number of sectors. GANs combine two neural networks that compete against one …

Artificial intelligence in predicting mechanical properties of composite materials

F Kibrete, T Trzepieciński, HS Gebremedhen… - Journal of Composites …, 2023 - mdpi.com
The determination of mechanical properties plays a crucial role in utilizing composite
materials across multiple engineering disciplines. Recently, there has been substantial …

Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties

NN Vlassis, WC Sun - Computer Methods in Applied Mechanics and …, 2023 - Elsevier
We introduce a denoising diffusion algorithm to discover microstructures with nonlinear fine-
tuned properties. Denoising diffusion probabilistic models are generative models that use …

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

A Henkes, H Wessels - Computer Methods in Applied Mechanics and …, 2022 - Elsevier
Multiscale simulations are demanding in terms of computational resources. In the context of
continuum micromechanics, the multiscale problem arises from the need of inferring …

Microstructure generation via generative adversarial network for heterogeneous, topologically complex 3D materials

T Hsu, WK Epting, H Kim, HW Abernathy, GA Hackett… - Jom, 2021 - Springer
Using a large-scale, experimentally captured 3D microstructure data set, we implement the
generative adversarial network (GAN) framework to learn and generate 3D microstructures …

Virtual microstructure design for steels using generative adversarial networks

JW Lee, NH Goo, WB Park, M Pyo… - Engineering …, 2021 - Wiley Online Library
The prediction of macro‐scale materials properties from microstructures, and vice versa,
should be a key part in modeling quantitative microstructure‐physical property relationships …