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

Residual-based adaptivity for two-phase flow simulation in porous media using Physics-informed Neural Networks

(2022) John M. Hanna et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

(2022) Jeremy Yu et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A physics-informed deep convolutional neural network for simulating and predicting transient Darcy flows in heterogeneous reservoirs without labeled data

(2022) Zhao Zhang   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Physics-informed neural networks for phase-field method in two-phase flow

(2022) Rundi Qiu et al.   PHYSICS OF FLUIDS

Thermodynamically‐consistent flash calculation in energy industry: From iterative schemes to a unified thermodynamics‐informed neural network

(2022) Tao Zhang et al.   INTERNATIONAL JOURNAL OF ENERGY RESEARCH

Physics-informed neural networks for solving Reynolds-averaged Navier–Stokes equations

(2022) Hamidreza Eivazi et al.   PHYSICS OF FLUIDS

Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network

(2022) Nanzhe Wang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

DiscretizationNet: A machine-learning based solver for Navier–Stokes equations using finite volume discretization

(2021) Rishikesh Ranade et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Three ways to solve partial differential equations with neural networks — A review

(2021) Jan Blechschmidt et al.   GAMM Mitteilungen

Thermodynamics-Informed Neural Network (TINN) for Phase Equilibrium Calculations Considering Capillary Pressure

(2021) Tao Zhang et al.   Energies

Prediction of porous media fluid flow using physics informed neural networks

(2021) Muhammad M. Almajid et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Efficient uncertainty quantification for dynamic subsurface flow with surrogate by Theory-guided Neural Network

(2020) Nanzhe Wang et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

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

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

DLBI: deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy

(2018) Yu Li et al.   BIOINFORMATICS

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

Algebraic Multigrid Domain and Range Decomposition (AMG-DD/AMG-RD)

(2015) R. Bank et al.   SIAM JOURNAL ON SCIENTIFIC COMPUTING