Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flow

Integration of Adversarial Autoencoders With Residual Dense Convolutional Networks for Estimation of Non‐Gaussian Hydraulic Conductivities

(2020) Shaoxing Mo et al.   WATER RESOURCES RESEARCH

Generative adversarial network as a stochastic subsurface model reconstruction

(2020) Leonardo Azevedo et al.   COMPUTATIONAL GEOSCIENCES

Surrogate permeability modelling of low-permeable rocks using convolutional neural networks

(2020) Jianwei Tian et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems

(2020) Meng Tang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A self-adaptive deep learning algorithm for accelerating multi-component flash calculation

(2020) Tao Zhang et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Deep-learning-based surrogate model for reservoir simulation with time-varying well controls

(2020) Zhaoyang Larry Jin et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Deep autoregressive neural networks for high‐dimensional inverse problems in groundwater contaminant source identification

(2019) Shaoxing Mo et al.   WATER RESOURCES RESEARCH

A Deep-Learning-Based Geological Parameterization for History Matching Complex Models

(2019) Yimin Liu et al.   Mathematical Geosciences

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

(2019) Yinhao Zhu et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Towards a robust parameterization for conditioning facies models using deep variational autoencoders and ensemble smoother

(2019) Smith W.A. Canchumuni et al.   COMPUTERS & GEOSCIENCES

Gradient-based deterministic inversion of geophysical data with generative adversarial networks: Is it feasible?

(2019) Eric Laloy et al.   COMPUTERS & GEOSCIENCES

Multilevel Strategies and Geological Parameterizations for History Matching Complex Reservoir Models

(2019) Yimin Liu et al.   SPE JOURNAL

Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

(2019) Luning Sun et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Physics-informed neural networks for high-speed flows

(2019) Zhiping Mao et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification

(2018) Yinhao Zhu et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Training-Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network

(2018) Eric Laloy et al.   WATER RESOURCES RESEARCH

A unified deep artificial neural network approach to partial differential equations in complex geometries

(2018) Jens Berg et al.   NEUROCOMPUTING

Solving high-dimensional partial differential equations using deep learning

(2018) Jiequn Han et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A New Data-Space Inversion Procedure for Efficient Uncertainty Quantification in Subsurface Flow Problems

(2017) Wenyue Sun et al.   Mathematical Geosciences

LSTM: A Search Space Odyssey

(2017) Klaus Greff et al.   IEEE Transactions on Neural Networks and Learning Systems

What the collapse of the ensemble Kalman filter tells us about particle filters

(2017) Matthias Morzfeld et al.   TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY

Ensemble smoother with multiple data assimilation

(2012) Alexandre A. Emerick et al.   COMPUTERS & GEOSCIENCES