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English(EN) Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation

新的多项式增强神经网络增强函数和偏微分方程逼近

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种名为多项式增强神经网络（PANNs）的新架构，它将深度神经网络（DNNs）与多项式展开相结合。这种混合方法旨在利用DNNs在高维逼近方面的灵活性以及多项式在光滑函数逼近方面的快速收敛性。PANNs结合了正交约束以实现稳定的训练和提高准确性，一种基剪枝方法来管理维度，以及一种多项式预处理策略。实验表明，PANNs在逼近光滑和非光滑函数以及求解偏微分方程方面优于标准DNNs。 AI

影响 这种新颖的架构可以提高AI模型在复杂逼近任务和科学模拟中的准确性和效率。

排序理由 该集群包含一篇详细介绍新模型架构的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的多项式增强神经网络增强函数和偏微分方程逼近

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该集群包含一篇详细介绍新模型架构的学术论文。[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) · Madison Cooley, Shandian Zhe, Robert M. Kirby, Varun Shankar ·

    具有弱正交约束的增强多项式神经网络 (PANNs) 用于增强函数和 PDE 近似

    arXiv:2406.02336v3 Announce Type: replace Abstract: We present polynomial-augmented neural networks (PANNs), a novel machine learning architecture that combines deep neural networks (DNNs) with polynomial expansions. PANNs combine the strengths of DNNs (flexibility and efficiency…

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