哈密尔顿神经网络具有自动对称性检测
Eva Dierkes1, Christian Offen2, Sina Ober-Blöbaum2
1Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany.
Chaos (Woodbury, N.Y.)
|June 5, 2023
概括
哈密尔顿神经网络 (HNN) 现在使用李代数嵌入系统对称性,使对称性动作和能量同时学习. 这增强了数据驱动的物理模型,用于像子和天体力学这样的系统.
科学领域:
- 计算物理 计算物理
- 机器学习 机器学习
- 动态系统 动态系统
背景情况:
- 哈密尔顿神经网络 (HNN) 将物理知识集成到哈密尔顿系统的数据驱动模型中.
- 保持交错结构是HNN的一个关键特征.
- 将对称性纳入HNN需要专门的方法.
研究的目的:
- 通过结合李代数框架来增强哈密尔顿神经网络 (HNN),用于对称性检测和嵌入.
- 能够同时学习对称群的动作和神经网络内的总能量.
- 提高复杂动态系统的数据驱动模型的准确性和物理现实性.
主要方法:
- 使用李代数框架开发了一个增强的HNN架构.
- 实施了检测和嵌入对称性直接进入神经网络的方法.
- 应用了增强的HNN来模拟一辆车上的子和一个两体天体动力学问题.
主要成果:
- 通过李代数成功集成对称性检测和嵌入HNN.
- 展示了对称群动作和系统能量的同时学习.
- 验证了基准物理系统的方法,显示了改进的建模能力.
结论:
- 李代数增强的HNN提供了一个强大的框架,用于对称的物理系统的数据驱动学习.
- 这种方法保留了基本的物理特性,如能量和对称性,从而导致更强大的模型.
- 这种方法适用于各种领域,包括机器人学,天体力学和分子动力学.
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