Analyzing the efficiency and robustness of deep convolutional neural networks for modeling natural convection in heterogeneous porous media

Filtered Selective Search and Evenly Distributed Convolutional Neural Networks for Casting Defects Recognition

(2021) Xiaoyuan Ji et al.   JOURNAL OF MATERIALS PROCESSING TECHNOLOGY

Physics- and image-based prediction of fluid flow and transport in complex porous membranes and materials by deep learning

(2021) Serveh Kamrava et al.   JOURNAL OF MEMBRANE SCIENCE

Deep residual U-net convolution neural networks with autoregressive strategy for fluid flow predictions in large-scale geosystems

(2021) Zhihao Jiang et al.   ADVANCES IN WATER RESOURCES

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

(2021) Meng Tang et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Conditioning of deep-learning surrogate models to image data with application to reservoir characterization

(2021) Cong Xiao et al.   KNOWLEDGE-BASED SYSTEMS

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

(2020) Shaoxing Mo et al.   WATER RESOURCES RESEARCH

Machine Learning in Geo- and Environmental Sciences: From Small to Large Scale

(2020) Pejman Tahmasebi et al.   ADVANCES IN WATER RESOURCES

Physics‐Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems

(2020) A. M. Tartakovsky et al.   WATER RESOURCES RESEARCH

Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport

(2020) QiZhi He et al.   ADVANCES IN WATER RESOURCES

Predicting groundwater depth fluctuations using deep learning, extreme learning machine and Gaussian process: a comparative study

(2020) Deepak Kumar et al.   Earth Science Informatics

DeePore: A deep learning workflow for rapid and comprehensive characterization of porous materials

(2020) Arash Rabbani et al.   ADVANCES IN WATER RESOURCES

Slice-to-voxel stochastic reconstructions on porous media with hybrid deep generative model

(2020) Fan Zhang et al.   COMPUTATIONAL MATERIALS SCIENCE

Uncertainty quantification and global sensitivity analysis of double-diffusive natural convection in a porous enclosure

(2020) Mohammad Mahdi Rajabi et al.   INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER

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

(2019) Shaoxing Mo et al.   WATER RESOURCES RESEARCH

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

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

Uncertainty quantification in three dimensional natural convection using polynomial chaos expansion and deep neural networks

(2019) Shantanu Shahane et al.   INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER

Velocity Field Estimation on Density‐Driven Solute Transport With a Convolutional Neural Network

(2019) Philipp J. Kreyenberg et al.   WATER RESOURCES RESEARCH

Evaluation of the reliability of a heat and mass transfer model in hygroscopic material

(2019) Julien Berger et al.   INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER

Linking Morphology of Porous Media to Their Macroscopic Permeability by Deep Learning

(2019) Serveh Kamrava et al.   TRANSPORT IN POROUS MEDIA

A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils

(2019) Haizhou Wu et al.   COMPUTERS & FLUIDS

Predicting Effective Diffusivity of Porous Media from Images by Deep Learning

(2019) Haiyi Wu et al.   Scientific Reports

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

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

Recent advances in convolutional neural networks

(2018) Jiuxiang Gu et al.   PATTERN RECOGNITION

Predicting the effective thermal conductivities of composite materials and porous media by machine learning methods

(2018) Han Wei et al.   INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER

A trans-disciplinary review of deep learning research and its relevance for water resources scientists

(2018) Chaopeng Shen   WATER RESOURCES RESEARCH

Entropy-based pruning method for convolutional neural networks

(2018) Cheonghwan Hur et al.   JOURNAL OF SUPERCOMPUTING

Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media

(2018) Shaoxing Mo et al.   WATER RESOURCES RESEARCH

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

Prospective Interest of Deep Learning for Hydrological Inference

(2017) Jean Marçais et al.   Groundwater

Analyzing natural convection in porous enclosure with polynomial chaos expansions: Effect of thermal dispersion, anisotropic permeability and heterogeneity

(2017) Noura Fajraoui et al.   INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER

Uncertainty-based simulation-optimization using Gaussian process emulation: Application to coastal groundwater management

(2017) Mohammad Mahdi Rajabi et al.   JOURNAL OF HYDROLOGY

Prospective Interest of Deep Learning for Hydrological Inference

(2017) Jean Marçais et al.   Groundwater

The influence of facies heterogeneity on the doublet performance in low-enthalpy geothermal sedimentary reservoirs

(2016) R.A. Crooijmans et al.   GEOTHERMICS

Polynomial chaos expansions for uncertainty propagation and moment independent sensitivity analysis of seawater intrusion simulations

(2015) Mohammad Mahdi Rajabi et al.   JOURNAL OF HYDROLOGY

Estimation of thermal and flow fields due to natural convection using support vector machines (SVM) in a porous cavity with discrete heat sources

(2008) Yasin Varol et al.   INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER