Summary of A Reinforcement Learning Strategy to Automate and Accelerate H/p-multigrid Solvers, by David Huergo et al.
A reinforcement learning strategy to automate and accelerate h/p-multigrid solvers
by David Huergo, Laura Alonso, Saumitra Joshi, Adrian Juanicoteca, Gonzalo Rubio, Esteban Ferrer
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Physics (physics.comp-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed reinforcement learning strategy aims to automate and accelerate high-order solvers using h/p-multigrid methods. By leveraging a proximal policy optimization algorithm, the paper seeks to automatically tune numerical parameters such as smoothing sweeps per level and correction fraction. This approach targets improved stability and efficiency in multigrid strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a way to use artificial intelligence to adjust settings for high-performance calculations. They used an algorithm that can learn from its mistakes to optimize parameters like the number of times to refine solutions at different levels. This helps make the calculation process more stable and efficient. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
