Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Parametrization of Stochastic Inputs Using Generative Adversarial Networks With Application in Geology
Shing Chan, Ahmed H. Elsheikh
Frontiers in Water (2020) Vol. 2
Open Access | Times Cited: 35

Showing 1-25 of 35 citing articles:

Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flow
Meng Tang, Y. Liu, Louis J. Durlofsky
Computer Methods in Applied Mechanics and Engineering (2021) Vol. 376, pp. 113636-113636
Open Access | Times Cited: 121
Subsurface sedimentary structure identification using deep learning: A review
Chuanjun Zhan, Zhenxue Dai, Zhijie Yang, et al.
Earth-Science Reviews (2023) Vol. 239, pp. 104370-104370
Closed Access | Times Cited: 44
Parametric generation of conditional geological realizations using generative neural networks
Shing Chan, Ahmed H. Elsheikh
Computational Geosciences (2019) Vol. 23, Iss. 5, pp. 925-952
Open Access | Times Cited: 111
A review of geostatistical simulation models applied to satellite remote sensing: Methods and applications
Fatemeh Zakeri, Grégoire Mariethoz
Remote Sensing of Environment (2021) Vol. 259, pp. 112381-112381
Open Access | Times Cited: 66
Data‐Worth Analysis for Heterogeneous Subsurface Structure Identification With a Stochastic Deep Learning Framework
Chuanjun Zhan, Zhenxue Dai, Mohamad Reza Soltanian, et al.
Water Resources Research (2022) Vol. 58, Iss. 11
Open Access | Times Cited: 64
3D CNN-PCA: A deep-learning-based parameterization for complex geomodels
Y. Liu, Louis J. Durlofsky
Computers & Geosciences (2020) Vol. 148, pp. 104676-104676
Open Access | Times Cited: 61
Hydrogeophysical Characterization of Nonstationary DNAPL Source Zones by Integrating a Convolutional Variational Autoencoder and Ensemble Smoother
Xueyuan Kang, Amalia Kokkinaki, Peter K. Kitanidis, et al.
Water Resources Research (2021) Vol. 57, Iss. 2
Closed Access | Times Cited: 41
Automatic reconstruction method of 3D geological models based on deep convolutional generative adversarial networks
Zixiao Yang, Qiyu Chen, Zhesi Cui, et al.
Computational Geosciences (2022) Vol. 26, Iss. 5, pp. 1135-1150
Closed Access | Times Cited: 33
Deep convolutional generative adversarial networks for modeling complex hydrological structures in Monte-Carlo simulation
Qiyu Chen, Zhesi Cui, Gang Liu, et al.
Journal of Hydrology (2022) Vol. 610, pp. 127970-127970
Closed Access | Times Cited: 29
Efficient estimation of groundwater contaminant source and hydraulic conductivity by an ILUES framework combining GAN and CNN
Na Zheng, Simin Jiang, Xuemin Xia, et al.
Journal of Hydrology (2023) Vol. 621, pp. 129677-129677
Closed Access | Times Cited: 18
Generation of non-stationary stochastic fields using generative adversarial networks
Alhasan Abdellatif, Ahmed H. Elsheikh, Daniel Busby, et al.
Frontiers in Earth Science (2025) Vol. 13
Open Access
Integration of Adversarial Autoencoders With Residual Dense Convolutional Networks for Estimation of Non‐Gaussian Hydraulic Conductivities
Shaoxing Mo, Nicholas Zabaras, Xiaoqing Shi, et al.
Water Resources Research (2020) Vol. 56, Iss. 2
Open Access | Times Cited: 42
Application of Bayesian Generative Adversarial Networks to Geological Facies Modeling
Runhai Feng, Darío Graña, Tapan Mukerji, et al.
Mathematical Geosciences (2022) Vol. 54, Iss. 5, pp. 831-855
Closed Access | Times Cited: 22
Characterizing Subsurface Structures From Hard and Soft Data With Multiple‐Condition Fusion Neural Network
Zhesi Cui, Qiyu Chen, Jian Luo, et al.
Water Resources Research (2024) Vol. 60, Iss. 11
Open Access | Times Cited: 4
Characterization of Subsurface Hydrogeological Structures With Convolutional Conditional Neural Processes on Limited Training Data
Zhesi Cui, Qiyu Chen, Gang Liu
Water Resources Research (2022) Vol. 58, Iss. 12
Closed Access | Times Cited: 16
Weight initialization algorithm for physics-informed neural networks using finite differences
Homayoon Tarbiyati, Behzad Nemati Saray
Engineering With Computers (2023) Vol. 40, Iss. 3, pp. 1603-1619
Closed Access | Times Cited: 9
Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties
Teeratorn Kadeethum, Daniel O’Malley, Youngsoo Choi, et al.
Computers & Geosciences (2022) Vol. 167, pp. 105212-105212
Open Access | Times Cited: 13
Integration of DDPM and ILUES for Simultaneous Identification of Contaminant Source Parameters and Non‐Gaussian Channelized Hydraulic Conductivity Field
Xun Zhang, Simin Jiang, Na Zheng, et al.
Water Resources Research (2024) Vol. 60, Iss. 9
Open Access | Times Cited: 2
Recursive convolutional neural networks in a multiple-point statistics framework
Sebastian Avalos, Julián M. Ortíz
Computers & Geosciences (2020) Vol. 141, pp. 104522-104522
Closed Access | Times Cited: 18
Verification of a real-time ensemble-based method for updating earth model based on GAN
Kristian Fossum, Sergey Alyaev, Jan Tveranger, et al.
Journal of Computational Science (2022) Vol. 65, pp. 101876-101876
Open Access | Times Cited: 11
Identification of contaminant source and hydraulic conductivity field based on an ILUES-SOM surrogate model
Na Zheng, Jinbing Liu, Xuemin Xia, et al.
Stochastic Environmental Research and Risk Assessment (2023) Vol. 37, Iss. 7, pp. 2725-2738
Open Access | Times Cited: 5
Generating unrepresented proportions of geological facies using Generative Adversarial Networks
Alhasan Abdellatif, Ahmed H. Elsheikh, Gavin H. Graham, et al.
Computers & Geosciences (2022) Vol. 162, pp. 105085-105085
Open Access | Times Cited: 8
Improving the training performance of Generative Adversarial Networks with limited data: application to the generation of geological models
Paulo Henrique Ranazzi, Xiaodong Luo, Márcio Augusto Sampaio
Computers & Geosciences (2024), pp. 105747-105747
Closed Access | Times Cited: 1
Generative Adversarial Network Applied to Ore Type Modeling in Complex Geological Environments
Helga Jordão, Leonardo Azevedo, A. J. Sousa, et al.
Mathematical Geosciences (2022) Vol. 54, Iss. 7, pp. 1165-1182
Closed Access | Times Cited: 7
Probabilistic Integration of Geomechanical and Geostatistical Inferences for Mapping Natural Fracture Networks
Akshat Chandna, Sanjay Srinivasan
Mathematical Geosciences (2023) Vol. 55, Iss. 5, pp. 645-671
Closed Access | Times Cited: 3
Page 1 - Next Page

Scroll to top