A physics-informed convolutional neural network for the simulation and prediction of two-phase Darcy flows in heterogeneous porous media
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Publication:2681146
DOI10.1016/j.jcp.2023.111919OpenAlexW4317214033MaRDI QIDQ2681146
Renmin Han, Zhao Zhang, Piyang Liu, Sheng Wang, Kai Zhang, Xia Yan
Publication date: 10 February 2023
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jcp.2023.111919
Uses Software
Cites Work
- Efficient uncertainty quantification for dynamic subsurface flow with surrogate by theory-guided neural network
- Discretizationnet: a machine-learning based solver for Navier-Stokes equations using finite volume discretization
- PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
- Residual-based adaptivity for two-phase flow simulation in porous media using physics-informed neural networks
- Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Algebraic Multigrid Domain and Range Decomposition (AMG-DD/AMG-RD)
- Solving high-dimensional partial differential equations using deep learning
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