Stochastic reconstruction of 3D porous media from 2D images using generative adversarial networks

Valsecchi, Andrea; Damas, Sergio; Tubilleja, Cristina; Arechalde, Javier

doi:10.1016/j.neucom.2019.12.040

Cited by 39 publications

(17 citation statements)

References 11 publications

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“…[168][169][170][171][172][173][174][175] In conjunction with the experimental techniques and mathematical models introduced in Section 3, AI techniques have been successfully employed in the study of energy materials. Various machine learning (ML) methods and advanced deep neural networks (DNN) have shown excellent performance in regard to material structure reconstruction and generation, and property and performance prediction, such as artificial neural networks (ANN), 174 support vector machines (SVM), 176 convolutional neural networks (CNN), [177][178][179][180][181][182] generative adversarial neural networks (GANN) [183][184][185][186][187][188][189][190] and so on. Fig.…”

Section: The Future Of Energy Materials: Digitalisation Of Porous Energy Materials Design and Optimisationmentioning

confidence: 99%

“…Since GANNs can be applied to both 2D and 3D images, a more convenient way to generate novel and realistic microstructures is to use only 2D images which are easy for users to assess. Valsecchi et al 190 developed a GANN to generate 3D porous sandstones from 2D images. The key point of this approach is that the discriminator operates on 2D images, while the generator produces 3D images.…”

Section: Structure Reconstruction and Generationmentioning

confidence: 99%

“…108,113 The recently successful applications of AI in videos and graphics have opened new opportunities in microstructure reconstruction and stochastic generation based on learning from digitised experimental images of physical structures. 177,178,[183][184][185][186][187][188][189][190] In this respect, there are two popular AI techniques that substantially accelerate the development, i.e. convolutional neural networks (CNN) and generative adversarial neural networks (GANN).…”

Section: Structure Reconstruction and Generationmentioning

confidence: 99%

See 2 more Smart Citations

Towards the digitalisation of porous energy materials: evolution of digital approaches for microstructural design

Niu

Pinfield

et al. 2021

Energy Environ. Sci.

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Section: The Future Of Energy Materials: Digitalisation Of Porous Energy Materials Design and Optimisationmentioning

confidence: 99%

Section: Structure Reconstruction and Generationmentioning

confidence: 99%

Section: Structure Reconstruction and Generationmentioning

confidence: 99%

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Towards the digitalisation of porous energy materials: evolution of digital approaches for microstructural design

Niu

Pinfield

et al. 2021

Energy Environ. Sci.

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“…Mosser et al (2017) firstly used DCGANs to reconstruct 3D porous structures of sandstone and carbonate. After their ground-breaking work, DCGANs rapidly swept the community and became an important approach for digital rock reconstruction (Mosser et al, 2018;Liu et al, 2019;Volkhonskiy et al, 2019;Valsecchi et al, 2020). Inspired by this, various GAN models have also been used, including but not limited to conditional GAN (Feng et al, 2019;Volkhonskiy et al, 2019), style GAN (Fokina et al, 2020), progressively growing GAN (Zheng and Zhang, 2020), Bicycle GAN (Feng, et al, 2020), as well as some hybrid models combining variational autoencoder and DCGAN (Shams et al, 2020;.…”

Section: Introductionmentioning

confidence: 99%

Digital Rock Reconstruction Using Wasserstein GANs with Gradient Penalty

Zhu

et al. 2022

Day 1 Mon, February 21, 2022

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Due to the scarcity and vulnerability of physical rock samples, digital rock reconstruction plays an important role in the numerical study of reservoir rock properties and fluid flow behaviors. With the rapid development of deep learning technologies, generative adversarial networks (GANs) have become a promising alternative to reconstruct complex pore structures. Numerous GAN models have been applied in this field, but many of them suffer the unstable training issue. In this study, we apply the Wasserstin GAN with gradient penalty, also known as the WGAN-GP network, to reconstruct Berea sandstone and Ketton limestone. Unlike many other GANs using the Jesnen-Shannon divergence, the WGAN-GP network exhibits a stable training performance by using the Wasserstin distance to measure the difference between generated and real data distributions. Moreover, the generated image quality correlates with the discriminator loss. This provides us an indicator of the training state instead of frequently subjective assessments in the training of deep convolutional GAN (DCGAN) based models. An integrated framework is presented to automate the entire workflow, including training set generation, network setup, model training and synthetic rock validation. Numerical results show that the WGAN-GP network accurately reconstructs both Berea sandstone and Ketton limestone in terms of two-point correlation and morphological properties.

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confidence: 99%

Computer Vision Analysis on Material Characterization Images

Cheng

Sha

Zuo

et al. 2021

Advanced Intelligent Systems

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Material characterization has been proved to be the most intuitive approach to understand the chemical composition, structure, and microstructure of materials, which is the basis of material design. One of the most important steps in material design is to extract the characteristics from an image, and find their associations with the material structure and properties. Therefore, in recent years, with the rapid development of machine vision algorithms, characterization images have attracted attention in the field of material characterization. Researchers use computer vision algorithms, such as image denoising and enhancement, to preprocess the representation image, image segmentation and classification to detect and separate each microstructure from the characterization image, and quantitatively analyze the properties of materials. Herein, the application of computer vision algorithms in material image representation is summarized and discussed. The latest and valuable views for experts and scholars in both computer vision and material grounds are presented. Thus, this review provides guidance for material exploration and promotes the developments of artificial intelligence in the field of materials.

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Stochastic reconstruction of 3D porous media from 2D images using generative adversarial networks

Cited by 39 publications

References 11 publications

Towards the digitalisation of porous energy materials: evolution of digital approaches for microstructural design

Towards the digitalisation of porous energy materials: evolution of digital approaches for microstructural design

Digital Rock Reconstruction Using Wasserstein GANs with Gradient Penalty

Computer Vision Analysis on Material Characterization Images

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