English(EN) RINS: Residual-Image Neural Subspace Solvers for Large Sparse Linear Systems

新型神经网络求解器RINS加速大型稀疏线性系统的求解

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

研究人员开发了RINS，一种新颖的神经网络子空间求解器，旨在解决来自偏微分方程离散化的大型稀疏线性系统。该方法，特别是Gate-RINS变体，从残差探测生成多项式校正基，并用一个轻量级门进行调制。该系统旨在通过将算子图像子空间与当前残差对齐来提高收敛速度，与传统的GMRES方法和仅图神经网络基线相比，在各种基准任务中更快地达到固定的相对残差阈值。 AI

影响这种新方法可以显著加速依赖于求解大型稀疏线性系统的科学模拟和复杂计算。

排序理由该集群包含一篇详细介绍新计算方法的学术论文。[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) · Zhongyan Ouyang, Weixin Liao, Mingquan Feng, Yehui Tang, Junchi Yan · 2026-10-05 04:00

RINS: 稀疏线性系统残差图像神经网络子空间求解器

arXiv:2610.02217v1 Announce Type: cross Abstract: Large sparse linear systems from PDE discretizations require correction subspaces whose operator images explain the current residual. We study this residual-image viewpoint and propose Gate-RINS, a neural subspace solver that gene…

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报道来源 [1]

RINS: 稀疏线性系统残差图像神经网络子空间求解器

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