几何神经普通微分方程:从多元组到李组
Yannik P Wotte1, Federico Califano1, Stefano Stramigioli1
1Robotics and Mechatronics, EEMCS, University of Twente (UT), Drienerlolaan 5, 7522 NB Enschede, The Netherlands.
Entropy (Basel, Switzerland)
|August 28, 2025
概括
神经普通微分方程 (神经ODE) 扩展到可微分的多元体和Lie组,使复杂的动态系统能够优化超出标准的欧几里德空间.
科学领域:
- 动态系统
- 机器学习
- 微分几何学
背景情况:
- 神经普通微分方程 (神经ODE) 广泛用于动态系统中的参数优化.
- 现有的神经ODE理论结果主要集中在欧几里德空间 (Rn) 上.
- 许多现实世界的动态系统在更复杂的空间上演变,如可微分的多元体和Lie组.
研究的目的:
- 将神经ODE的理论框架扩展到可微分的多元组.
- 为多个神经ODE提供现有结果的统一导出.
- 为在Lie组上演变的系统引入神经ODE的扩展.
主要方法:
- 收集和统一最近的神经ODEs的理论结果.
- 开发一个用于将现有的神经ODE方法扩展到可微分分组的教学框架.
- 扩展多重的神经ODEs以利用李群的结构.
主要成果:
- 介绍了神经ODE在可分化的多元体上的统一导出.
- 该框架被证明适用于各种动态系统.
- 为Lie组开发了多重神经ODEs的新扩展,利用它们的特定结构.
结论:
- 神经ODE可以有效地将其推广到可微分的多元组和Lie组.
- 这种概括扩大了神经ODE的适用性到更广泛的复杂动态系统.
- 这些方法为未来的几何深度学习和基于物理的神经网络的研究提供了途径.
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