SympGNNs:用于识别高维哈密尔顿系统和节点分类的Symplectic Graph神经网络
Alan John Varghese1, Zhen Zhang2, George Em Karniadakis3
1School of Engineering, Brown University, Providence, RI 02912, USA.
概括
交叉图形神经网络 (SympGNNs) 有效地学习高维哈密尔顿系统,优于现有模型. 这种新方法还在节点分类方面表现出色,并解决了关键图形神经网络的挑战.
科学领域:
- 计算物理 计算物理
- 机器学习 机器学习
- 图形神经网络 图形神经网络
背景情况:
- 现有的神经网络模型与高维的哈密尔顿系统作斗争.
- 综合神经网络 (SympNets) 在低维度中是准确的,但在复杂的多体系统中失败.
研究的目的:
- 介绍对高维哈密尔顿系统的交叉图形神经网络 (SympGNNs).
- 增强系统识别和节点分类能力.
- 在物理模拟中解决当前神经网络模型的局限性.
主要方法:
- 开发了SympGNNs,将simplectic地图与 permutation等差结合起来.
- 提出了两个变体:G-SympGNN和LA-SympGNN,基于能量参数化.
- 将SympGNN应用于物理实例,如合波器和分子动力学.
主要成果:
- 在高维哈密尔顿系中,SympGNN有效地处理系统识别.
- 在节点分类任务中实现了最先进的准确性.
- 证明了在图形神经网络中克服过度平滑和异构性的能力.
结论:
- SympGNNs提供了一种强大的新方法来学习复杂的物理动态.
- 该方法在系统识别和节点分类方面都显示出前景.
- 在将图形神经网络应用于科学挑战方面,SympGNN代表了重大进展.
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