Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.15872 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929431843438592 |
|---|---|
| author | Huergo, David Alonso, Laura Joshi, Saumitra Juanicoteca, Adrian Rubio, Gonzalo Ferrer, Esteban |
| author_facet | Huergo, David Alonso, Laura Joshi, Saumitra Juanicoteca, Adrian Rubio, Gonzalo Ferrer, Esteban |
| contents | 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 the corrected solution that is transferred from a coarser grid to a finer grid). The objective of this paper is to use a proximal policy optimization algorithm to automatically tune the multigrid parameters and, by doing so, improve stability and efficiency of the h/p-multigrid strategy.
Our findings reveal that the proposed reinforcement learning h/p-multigrid approach significantly accelerates and improves the robustness of steady-state simulations for one dimensional advection-diffusion and nonlinear Burgers' equations, when discretized using high-order h/p methods, on uniform and nonuniform grids. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_15872 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A reinforcement learning strategy to automate and accelerate h/p-multigrid solvers Huergo, David Alonso, Laura Joshi, Saumitra Juanicoteca, Adrian Rubio, Gonzalo Ferrer, Esteban Machine Learning Computational Physics 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 the corrected solution that is transferred from a coarser grid to a finer grid). The objective of this paper is to use a proximal policy optimization algorithm to automatically tune the multigrid parameters and, by doing so, improve stability and efficiency of the h/p-multigrid strategy. Our findings reveal that the proposed reinforcement learning h/p-multigrid approach significantly accelerates and improves the robustness of steady-state simulations for one dimensional advection-diffusion and nonlinear Burgers' equations, when discretized using high-order h/p methods, on uniform and nonuniform grids. |
| title | A reinforcement learning strategy to automate and accelerate h/p-multigrid solvers |
| topic | Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2407.15872 |