Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non-Gaussian hydraulic conductivities

26 June 2019

Papers citing "Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non-Gaussian hydraulic conductivities"

9 / 9 papers shown

Title
Uncertainty quantification of two-phase flow in porous media via coupled-TgNN surrogate model Jun Yu Li Dongxiao Zhang Tianhao He Q. Zheng AI4CE 27 6 0 28 May 2022
Use of Multifidelity Training Data and Transfer Learning for Efficient Construction of Subsurface Flow Surrogate Models Su Jiang L. Durlofsky AI4CE 22 29 0 23 Apr 2022
Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties Zitong Zhou N. Zabaras D. Tartakovsky 26 16 0 24 Oct 2021
Applications of physics-informed scientific machine learning in subsurface science: A survey A. Sun H. Yoon C. Shih Zhi Zhong AI4CE 29 9 0 10 Apr 2021
3D CNN-PCA: A Deep-Learning-Based Parameterization for Complex Geomodels Yimin Liu L. Durlofsky AI4CE 24 60 0 16 Jul 2020
Data-Space Inversion Using a Recurrent Autoencoder for Time-Series Parameterization Su Jiang L. Durlofsky 27 18 0 30 Apr 2020
Multiphase flow prediction with deep neural networks Gege Wen Meng Tang S. Benson 16 6 0 21 Oct 2019
A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Meng Tang Yimin Liu L. Durlofsky AI4CE 32 257 0 16 Aug 2019
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network Wenzhe Shi Jose Caballero Ferenc Huszár J. Totz Andrew P. Aitken Rob Bishop Daniel Rueckert Zehan Wang SupR 207 5,176 0 16 Sep 2016