A reinforcement learning strategy to automate and accelerate h/p-multigrid solvers

A reinforcement learning strategy to automate and accelerate h/p-multigrid solvers

We explore a reinforcement learning strategy to automate and accelerate h/p-multigrid methods in high-order solvers. Multigrid methods are very efficient but require fine-tuning of numerical parameters, such as the number of smoothing sweeps per level and the correction fraction (i.e., proportion of...

Full description

Saved in:
Bibliographic Details
Main Authors: David Huergo, Laura Alonso, Saumitra Joshi, Adrian Juanicotena, Gonzalo Rubio, Esteban Ferrer
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Results in Engineering
Subjects:
Reinforcement learning
Proximal policy optimization
PPO
Advection-diffusion
Burgers' equation
High-order flux reconstruction
Online Access:http://www.sciencedirect.com/science/article/pii/S2590123024012040
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://www.sciencedirect.com/science/article/pii/S2590123024012040

Similar Items