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English(EN) RAPNet: Accelerating Algebraic Multigrid with Learned Sparse Corrections

新的GNN框架RAPNet加速代数多重网格求解器

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了RAPNet，一个新颖的图神经网络（GNN）框架，旨在加速代数多重网格（AMG）求解器。该框架解决了粗网格算子中稀疏性和收敛性质量之间的平衡问题，这是经典AMG方法的一个常见问题。RAPNet学习直接从代数系统中生成稀疏、鲁棒的粗算子，并采用逐层训练策略，以实现跨大域的有效泛化。该方法在求解器设置阶段运行，保持了求解阶段的效率，并在各种科学计算任务上优于传统基线。 AI

影响 这个新的GNN框架可以显著加速科学计算和图分析中的复杂模拟和分析。

排序理由 该集群包含一篇详细介绍加速科学计算求解器新方法的论文。

在 arXiv cs.LG 阅读 →

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新的GNN框架RAPNet加速代数多重网格求解器

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Yali Fink, Ido Ben-Yair, Lars Ruthotto, Eran Treister ·

    RAPNet：通过学习到的稀疏校正加速代数多重网格

    arXiv:2605.26854v1 Announce Type: new Abstract: The scalable solution of large sparse linear systems is a bottleneck in scientific computing and graph analysis. While algebraic multigrid (AMG) offers optimal linear scaling, its performance is severely constrained by the trade-off…

  2. arXiv cs.LG TIER_1 English(EN) · Eran Treister ·

    RAPNet：通过学习到的稀疏校正加速代数多重网格

    The scalable solution of large sparse linear systems is a bottleneck in scientific computing and graph analysis. While algebraic multigrid (AMG) offers optimal linear scaling, its performance is severely constrained by the trade-off between the sparsity and convergence quality of…

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