A reinforcement learning strategy to automate and accelerate h/p-multigrid solvers
<|reference_start|>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 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.<|reference_end|>
@article{huergo2024a,
    title={A reinforcement learning strategy to automate and accelerate
  h/p-multigrid solvers},
    author={David Huergo, Laura Alonso, Saumitra Joshi, Adrian Juanicoteca,
  Gonzalo Rubio and Esteban Ferrer},
    journal={arXiv preprint arXiv:2407.15872},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15872},
    primaryClass={cs.LG physics.comp-ph}
}