A case study on homogeneous and heterogeneous reservoir porous media reconstruction by using generative adversarial networks

Liu, Siyan; Zhi, Zhao; Takbiri-Borujeni, Ali; Kazemi, Mohammad; Fu, Qinwen; Yang, Yuhao

doi:10.1016/j.egypro.2019.01.493

Cited by 28 publications

(16 citation statements)

References 3 publications

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“…So, the suggested by Mosser et al [16] GAN approach is aimed at a quick generation of representative volume elements of the microstructure for the estimation of flow properties. However, further investigation [17] shows that its result is not representative. In our work, we use another GAN architecture and achieve a better result for two-dimensional reconstructions.…”

Section: Related Workmentioning

confidence: 83%

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Microstructure synthesis using style-based generative adversarial networks

et al. 2020

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Work considers the usage of StyleGAN architecture for the task of microstructure synthesis. The task is the following: given number of samples of structure we try to generate similar samples at the same time preserving its properties. Since the considered architecture is not able to produce samples of sizes larger than the training images, we propose to use image quilting to merge fixedsized samples. One of the key features of the considered architecture is that it uses multiple image resolutions. We also investigate the necessity of such an approach. arXiv:1909.07042v1 [eess.IV]

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Section: Related Workmentioning

confidence: 83%

“…1c) estimation of flow properties. However, further investigation [17] shows that its result is not representative. In our work, we use another GAN architecture and achieve a better result for two-dimensional reconstructions.…”

Section: Related Workmentioning

confidence: 83%

Microstructure synthesis using style-based generative adversarial networks

et al. 2020

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“…Other studies attempt to bridge the gap between microstructure and properties in generative tasks using trained black-box surrogates, such as Tan et al's work [64]. Most research focuses on 2D microstructure images, though several also consider 3D voxelizations [67,69].…”

Section: Microstructure Nanostructure and Metamaterialsmentioning

confidence: 99%

Deep Generative Models in Engineering Design: A Review

Regenwetter¹,

Nobari²,

Ahmed³

2021

Preprint

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Automated design synthesis has the potential to revolutionize the modern human design process and improve access to highly optimized and customized products across countless industries. Successfully adapting generative Machine Learning to design engineering may be the key to such automated design synthesis and is a research subject of great importance. We present a review and analysis of Deep Generative Learning models in engineering design. Deep Generative Models (DGMs) typically leverage deep networks to learn from an input dataset and learn to synthesize new designs. Recently, DGMs such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), feedforward Neural Networks (NNs) and certain Deep Reinforcement Learning (DRL) frameworks have shown promising results in design applications like structural optimization, materials design, and shape synthesis. The prevalence of DGMs in Engineering Design has skyrocketed since 2016. Anticipating continued growth, we conduct a review of recent advances with the hope of benefitting researchers interested in DGMs for design. We structure our review as an exposition of the algorithms, datasets, representation methods, and applications commonly used in the current literature. In particular, we discuss key works that have introduced new techniques and methods in DGMs, successfully applied DGMs to a design-related domain, or directly supported development of DGMs through datasets or auxiliary methods. We further identify key challenges and limitations currently seen in DGMs across design fields, such as design creativity, handling complex constraints and objectives, and modeling both form and functional performance simultaneously. In our discussion we identify possible solution pathways as key areas on which to target future work.

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“…(2017) cropped a 3D dataset of