English(EN) Graph Neural Multilevel Preconditioners for Iterative Solvers

新的图神经网络预处理器旨在改进科学求解器

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-31 04:00

研究人员开发了一种新的图神经网络多层预处理器（GMP），旨在提高大型稀疏线性系统迭代求解器的效率。该方法将代数多重网格（AMG）层次结构集成到图神经网络框架中，学习用于平滑、限制和插值的算子。在超过800个矩阵上与经典AMG和其他GNN预处理器进行了测试，GMP显示出在科学模拟中提高收敛性的潜力，但也注意到了其与单层方法相比的开销。 AI

影响这种新的预处理器可以通过提高复杂线性系统迭代求解器的效率来加速科学模拟。

排序理由详细介绍科学计算新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Zechen Zhang, Rui Peng Li, Yousef Saad · 2026-07-31 04:00

用于迭代求解器的图神经网络多层预处理器

arXiv:2607.28456v1 Announce Type: cross Abstract: Solving large, sparse linear systems is a core task in scientific computing, and efficient iterative solvers rely critically on effective and robust preconditioning. While classical methods such as algebraic multigrid (AMG) are hi…

本文如何被排名

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用于迭代求解器的图神经网络多层预处理器

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