论文标题
学习可逆的符号动态
Learning reversible symplectic dynamics
论文作者
论文摘要
在许多感兴趣的动态系统中，时间反转对称性自然是一种结构特性。尽管在机器学习中越来越多地认识到艰苦的对称性的重要性，但迄今为止，这已经避免了时间可逆性。在本文中，我们提出了一种新的神经网络体系结构，用于从数据中学习时间可逆的动力系统。由于它们在物理知识学习中的重要性，我们特别关注对符号系统的适应。
Time-reversal symmetry arises naturally as a structural property in many dynamical systems of interest. While the importance of hard-wiring symmetry is increasingly recognized in machine learning, to date this has eluded time-reversibility. In this paper we propose a new neural network architecture for learning time-reversible dynamical systems from data. We focus in particular on an adaptation to symplectic systems, because of their importance in physics-informed learning.
