Residual-based adaptivity for two-phase flow simulation in porous media using physics-informed neural networks
From MaRDI portal
Publication:2156788
DOI10.1016/j.cma.2022.115100OpenAlexW3202132106MaRDI QIDQ2156788
Ramzi Askri, Domenico Borzacchiello, John M. Hanna, Jose Vicente Aguado, Sebastien Comas-Cardona
Publication date: 20 July 2022
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2109.14290
Related Items (2)
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks ⋮ A physics-informed convolutional neural network for the simulation and prediction of two-phase Darcy flows in heterogeneous porous media
Cites Work
- Unnamed Item
- Tensor Decompositions and Applications
- Adaptive mesh refinement for hyperbolic partial differential equations
- Volume of fluid (VOF) method for the dynamics of free boundaries
- A formula for estimation of truncation errors of convection terms in a curvilinear coordinate system
- A level set approach for computing solutions to incompressible two-phase flow
- A posteriori error estimation for finite-volume solutions of hyperbolic conservation laws
- r-refinement grid adaptation algorithms and issues
- A unified approach to remeshing strategies for finite element \(h\)-adaptivity
- A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
- Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks
- Physics-informed neural networks for high-speed flows
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
- Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems
- Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture
- An overview of smoothed particle hydrodynamics for simulating multiphase flow
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Tensor representation of non-linear models using cross approximations
- Heuristic a posteriori estimation of error due to dissipation in finite volume schemes and application to mesh adaptation
- A combined rh‐adaptive scheme based on domain subdivision. Formulation and linear examples
- Nonlinear model reduction via a locally weighted POD method
- Smoothed particle hydrodynamics: theory and application to non-spherical stars
- Thepandh-pVersions of the Finite Element Method, Basic Principles and Properties
- DeepXDE: A Deep Learning Library for Solving Differential Equations
- Model Order Reduction Framework for Problems with Moving Discontinuities
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations
- fPINNs: Fractional Physics-Informed Neural Networks
- Computational Methods for Multiphase Flows in Porous Media
- Tucker Dimensionality Reduction of Three-Dimensional Arrays in Linear Time
This page was built for publication: Residual-based adaptivity for two-phase flow simulation in porous media using physics-informed neural networks
