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Found 4 papers, 3 papers with code
Two approaches to inpainting microstructure with deep convolutional generative adversarial networks
Imaging is critical to the characterisation of materials.
MicroLib: A library of 3D microstructures generated from 2D micrographs using SliceGAN
3D microstructural datasets are commonly used to define the geometrical domains used in finite element modelling.
Fusion of complementary 2D and 3D mesostructural datasets using generative adversarial networks
In this paper, we present a method for combining information from pairs of distinct but complementary imaging techniques in order to accurately reconstruct the desired multi-phase, high resolution, representative, 3D images.
Generating 3D structures from a 2D slice with GAN-based dimensionality expansion
Generative adversarial networks (GANs) can be trained to generate 3D image data, which is useful for design optimisation.
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