Structure preservation for the deep neural network multigrid solver

DOI10.1553/etna_vol56s86zbMath1487.65153arXiv2012.05290OpenAlexW3112158738MaRDI QIDQ2672194

Yanyan Li

Publication date: 8 June 2022

Published in: ETNA. Electronic Transactions on Numerical Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2012.05290

zbMATH Keywords

incompressible Navier-Stokes equationsmultigrid methodsartificial neural networksmachine learning

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Navier-Stokes equations for incompressible viscous fluids (76D05) Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs (65M60) Multigrid methods; domain decomposition for initial value and initial-boundary value problems involving PDEs (65M55)

Related Items (2)

Using deep neural networks for detecting spurious oscillations in discontinuous Galerkin solutions of convection-dominated convection-diffusion equations ⋮ A priori and a posteriori error estimates for the deep Ritz method applied to the Laplace and Stokes problem

Uses Software

• GASCOIGNE
• PyTorch
• ckn_kernel
• DeepXDE
• PDE-Net
• DeepONet

Cites Work

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