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Neural networks leverage power iteration for eigenvalue problems

By PulseAugur Editorial · [1 sources] ·

Researchers have developed novel neural networks inspired by the power method and inverse power method to solve linear eigenvalue problems. These networks leverage automatic differentiation to implement differential operators and learn eigenfunctions through optimized loss functions. The methods have demonstrated accurate approximations for eigenvalues and eigenfunctions in numerical experiments across various dimensions. AI

IMPACT Introduces a new neural network approach for solving complex mathematical problems, potentially aiding scientific research.

RANK_REASON Academic paper detailing a novel methodology for solving mathematical problems using neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Neural networks leverage power iteration for eigenvalue problems

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Academic paper detailing a novel methodology for solving mathematical problems using neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [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…

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