Accelerating high order discontinuous Galerkin solvers using neural networks: 3D compressible Navier-Stokes equations-Reference-Cited by-同舟云学术

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

Published:2023-09 Issue: Volume:489 Page:112253
ISSN:0021-9991
Container-title:Journal of Computational Physics
language:en
Short-container-title:Journal of Computational Physics

Author:

Manrique de Lara Fernando,Ferrer Esteban^ORCID

Publisher

Elsevier BV

Subject

Computer Science Applications,Physics and Astronomy (miscellaneous),Applied Mathematics,Computational Mathematics,Modeling and Simulation,Numerical Analysis

Reference36 articles.

1. Accelerating high order discontinuous Galerkin solvers using neural networks: 1d Burgers' equation;Manrique de Lara;Comput. Fluids,2022

2. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control;Brunton,2019

3. A review on deep reinforcement learning for fluid mechanics;Garnier;Comput. Fluids,2021

4. Enhancing computational fluid dynamics with machine learning;Vinuesa;Nat. Comput. Sci.,2022

5. Improving aircraft performance using machine learning: a review;Le Clainche;Aerosp. Sci. Technol.,2023

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Accelerating high order discontinuous Galerkin solvers using neural networks: Wall bounded flows;Journal of Physics: Conference Series;2024-04-01

2. DNN-MG: A hybrid neural network/finite element method with applications to 3D simulations of the Navier–Stokes equations;Computer Methods in Applied Mechanics and Engineering;2024-02

3. Artificial intelligence and machine learning in aerodynamics;Metascience in Aerospace;2024