A mixed pressure-velocity formulation to model flow in heterogeneous porous media with physics-informed neural networks

Stiff-PDEs and Physics-Informed Neural Networks

(2023) Prakhar Sharma et al.   ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING

An efficient discontinuous Galerkin - mixed finite element model for variable density flow in fractured porous media

(2023) Anis Younes et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A physics-informed convolutional neural network for the simulation and prediction of two-phase Darcy flows in heterogeneous porous media

(2023) Zhao Zhang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Physical information neural networks for 2D and 3D nonlinear Biot model and simulation on the pressure of brain

(2023) Hao Chen et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management

(2022) Jianchong Sun et al.   JOURNAL OF HYDROLOGY

GW-PINN: A deep learning algorithm for solving groundwater flow equations

(2022) Xiaoping Zhang et al.   ADVANCES IN WATER RESOURCES

Multi-output physics-informed neural networks for forward and inverse PDE problems with uncertainties

(2022) Mingyuan Yang et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Physics-Informed Neural Networks for Solving Coupled Stokes–Darcy Equation

(2022) Ruilong Pu et al.   Entropy

Physics-informed neural networks for inverse problems in supersonic flows

(2022) Ameya D. Jagtap et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Physics-Informed Neural Network Solution of Thermo–Hydro–Mechanical Processes in Porous Media

(2022) Danial Amini et al.   JOURNAL OF ENGINEERING MECHANICS

Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management

(2022) Aleksandra Pachalieva et al.   Scientific Reports

Upscaling the porosity–permeability relationship of a microporous carbonate for Darcy-scale flow with machine learning

(2021) H. P. Menke et al.   Scientific Reports

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

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

(2021) Mohammad Mahdi Rajabi et al.   INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER

Physics-constrained deep learning for solving seepage equation

(2021) Li Daolun et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

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

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

Deep learning of subsurface flow via theory-guided neural network

(2020) Nanzhe Wang et al.   JOURNAL OF HYDROLOGY

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

PPINN: Parareal physics-informed neural network for time-dependent PDEs

(2020) Xuhui Meng et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Physics‐informed neural networks with monotonicity constraints for Richardson‐Richards equation: Estimation of constitutive relationships and soil water flux density from volumetric water content measurements

(2020) Toshiyuki Bandai et al.   WATER RESOURCES RESEARCH

PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain

(2020) Han Gao et al.   JOURNAL OF COMPUTATIONAL PHYSICS

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

(2019) Shaoxing Mo et al.   WATER RESOURCES RESEARCH

Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

(2019) Dongkun Zhang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

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

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

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

Hidden physics models: Machine learning of nonlinear partial differential equations

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

Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas

(2018) Jianfeng Zhang et al.   JOURNAL OF HYDROLOGY

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

(2018) Eric Laloy et al.   WATER RESOURCES RESEARCH

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

An efficient numerical model for hydrodynamic parameterization in 2D fractured dual-porosity media

(2013) Hassane Fahs et al.   ADVANCES IN WATER RESOURCES

Mixed finite elements for solving 2-D diffusion-type equations

(2010) Anis Younes et al.   REVIEWS OF GEOPHYSICS