Efficient coupled deep neural networks for the time-dependent coupled Stokes-Darcy problems
DOI10.1016/j.amc.2022.127514OpenAlexW4296116669MaRDI QIDQ2096255
Publication date: 16 November 2022
Published in: Applied Mathematics and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.amc.2022.127514
Stokes problemscientific computingDarcy equationBeavers-Joseph-Saffman-Jones interface conditiondeep neural networks
Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Numerical methods for partial differential equations, boundary value problems (65Nxx) Flows in porous media; filtration; seepage (76Sxx)
Related Items (2)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A multiple-time-step technique for coupled free flow and porous medium systems
- A domain decomposition method for the time-dependent Navier-Stokes-Darcy model with Beavers-Joseph interface condition and defective boundary condition
- Coupled Stokes-Darcy model with Beavers-Joseph interface boundary condition
- A weak Galerkin finite element method for a coupled Stokes-Darcy problem on general meshes
- Discontinuous finite volume element method for a coupled non-stationary Stokes-Darcy problem
- The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
- DGM: a deep learning algorithm for solving partial differential equations
- Finite volume methods for the incompressible Navier-Stokes equations
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
- Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations
- Least squares approach for the time-dependent nonlinear Stokes-Darcy flow
- Sur l'approximation de la solution des équations de Navier-Stokes par la méthode des pas fractionnaires. II
- The deep learning Galerkin method for the general Stokes equations
- Nonlinear Degenerate Evolution Equations in Mixed Formulation
- Finite Element Approximations for Stokes–Darcy Flow with Beavers–Joseph Interface Conditions
- Relu Deep Neural Networks and Linear Finite Elements
- Numerical implementation of the Crank-Nicolson/Adams-Bashforth scheme for the time-dependent Navier-Stokes equations
- Decoupled schemes for a non-stationary mixed Stokes-Darcy model
- Neural‐network‐based approximations for solving partial differential equations
- A second‐order partitioned method with different subdomain time steps for the evolutionary Stokes‐Darcy system
- A decoupling method with different subdomain time steps for the nonstationary stokes–darcy model
- Analysis of Long Time Stability and Errors of Two Partitioned Methods for Uncoupling Evolutionary Groundwater--Surface Water Flows
- Solving high-dimensional partial differential equations using deep learning
- Solving parametric PDE problems with artificial neural networks
- Staggered DG Method for Coupling of the Stokes and Darcy--Forchheimer Problems
- Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations
- fPINNs: Fractional Physics-Informed Neural Networks
- Parallel, non-iterative, multi-physics domain decomposition methods for time-dependent Stokes-Darcy systems
This page was built for publication: Efficient coupled deep neural networks for the time-dependent coupled Stokes-Darcy problems