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English(EN) Neural Networks Based on Power Method and Inverse Power Method for Solving Linear Eigenvalue Problems

神经网络利用幂迭代法求解特征值问题

研究人员开发了受幂法和反幂法启发的Новые нейронные сети,用于求解线性特征值问题。这些网络利用自动微分来实现微分算子,并通过优化的损失函数学习特征函数。在不同维度的数值实验中,这些方法已证明能准确逼近特征值和特征函数。 AI

影响 引入了一种解决复杂数学问题的新神经网络方法,可能有助于科学研究。

排序理由 学术论文,详细介绍了使用神经网络解决数学问题的Новая методология。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

神经网络利用幂迭代法求解特征值问题

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学术论文,详细介绍了使用神经网络解决数学问题的Новая методология。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Qihong Yang, Yangtao Deng, Yu Yang, Qiaolin He, Shiquan Zhang ·

    Neural Networks Based on Power Method and Inverse Power Method for Solving Linear Eigenvalue Problems

    arXiv:2209.11134v5 Announce Type: cross Abstract: In this article, we propose two kinds of neural networks inspired by power method and inverse power method to solve linear eigenvalue problems. These neural networks share similar ideas with traditional methods, in which the diffe…