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.

翻译：时间反向对称自然作为许多动态相关系统中的结构属性而产生。虽然硬线对称的重要性在机器学习中日益得到承认,但迄今为止,这一直没有在时间上的可逆性。在本文件中,我们提出了一个新的神经网络架构,用于从数据中学习时间可逆的动态系统。我们特别侧重于对随机系统的适应,因为它们在物理学知情学习中的重要性。