Accelerating high order discontinuous Galerkin solvers using neural networks: 3D compressible Navier-Stokes equations

DOI10.1016/j.jcp.2023.112253arXiv2207.11571MaRDI QIDQ6107091

Esteban Ferrer, Fernando Manrique de Lara

Publication date: 3 July 2023

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2207.11571

zbMATH Keywords

Navier-Stokesdeep neural networkshigh order discontinuous GalerkinTaylor Green vortex

Mathematics Subject Classification ID

Basic methods in fluid mechanics (76Mxx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Fluid mechanics (76-XX)

Related Items

DNN-MG: a hybrid neural network/finite element method with applications to 3D simulations of the Navier-Stokes equations

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