Efficient coupled deep neural networks for the time-dependent coupled Stokes-Darcy problems
type: Item
instance of: scholarly article
title: Efficient coupled deep neural networks for the time-dependent coupled Stokes-Darcy problems
author: Jing Yue
author: Jian Li
published in: Applied Mathematics and Computation
publication date: 2022-11-16T00:00:00Z
Mathematics Subject Classification ID: 65Mxx
Mathematics Subject Classification ID: 76Sxx
Mathematics Subject Classification ID: 65Nxx
zbMATH DE Number: 7617926
zbMATH Keywords: scientific computing
zbMATH Keywords: deep neural networks
zbMATH Keywords: Stokes problem
zbMATH Keywords: Darcy equation
zbMATH Keywords: Beavers-Joseph-Saffman-Jones interface condition
describes a project that uses: FPINNs
describes a project that uses: DGM
describes a project that uses: PDE-Net
MaRDI profile type: Publication
full work available at URL: j.amc.2022.127514
OpenAlex ID: W4296116669
cites work: Q5678608
cites work: Decoupled schemes for a non-stationary mixed Stokes-Darcy model
cites work: Analysis of Long Time Stability and Errors of Two Partitioned Methods for Uncoupling Evolutionary Groundwater--Surface Water Flows
cites work: Parallel, non-iterative, multi-physics domain decomposition methods for time-dependent Stokes-Darcy systems
cites work: A decoupling method with different subdomain time steps for the nonstationary stokes–darcy model
cites work: A multiple-time-step technique for coupled free flow and porous medium systems
cites work: Least squares approach for the time-dependent nonlinear Stokes-Darcy flow
cites work: Finite volume methods for the incompressible Navier-Stokes equations
cites work: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
cites work: Solving parametric PDE problems with artificial neural networks
cites work: Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
cites work: Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations
cites work: The deep learning Galerkin method for the general Stokes equations
cites work: Q5019878
cites work: Solving high-dimensional partial differential equations using deep learning
cites work: The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
cites work: Relu Deep Neural Networks and Linear Finite Elements
cites work: Neural‐network‐based approximations for solving partial differential equations
cites work: DGM: a deep learning algorithm for solving partial differential equations
cites work: fPINNs: Fractional Physics-Informed Neural Networks
cites work: Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
cites work: Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations
cites work: Nonlinear Degenerate Evolution Equations in Mixed Formulation
cites work: Finite Element Approximations for Stokes–Darcy Flow with Beavers–Joseph Interface Conditions
cites work: A second‐order partitioned method with different subdomain time steps for the evolutionary Stokes‐Darcy system
cites work: Coupled Stokes-Darcy model with Beavers-Joseph interface boundary condition
cites work: Sur l'approximation de la solution des équations de Navier-Stokes par la méthode des pas fractionnaires. II
cites work: Staggered DG Method for Coupling of the Stokes and Darcy--Forchheimer Problems
cites work: A domain decomposition method for the time-dependent Navier-Stokes-Darcy model with Beavers-Joseph interface condition and defective boundary condition
cites work: A weak Galerkin finite element method for a coupled Stokes-Darcy problem on general meshes
cites work: Q5862239
cites work: Q3621233
cites work: Numerical implementation of the Crank-Nicolson/Adams-Bashforth scheme for the time-dependent Navier-Stokes equations
cites work: Discontinuous finite volume element method for a coupled non-stationary Stokes-Darcy problem
DOI: 10.1016/J.AMC.2022.127514
description: scientific article; zbMATH DE number 7617926