论文标题
AiPoincaré：轨迹的机器学习保护法
AI Poincaré: Machine Learning Conservation Laws from Trajectories
论文作者
论文摘要
我们提出了AiPoincaré，这是一种使用未知动力学系统的轨迹数据，用于自动发现保守数量的机器学习算法。我们在五个哈密顿系统（包括重力3体问题）上对其进行了测试，并发现它不仅发现了所有准确的保守量，还发现了周期性的轨道，相位过渡和分解时间标准，以实现近似保护定律。
We present AI Poincaré, a machine learning algorithm for auto-discovering conserved quantities using trajectory data from unknown dynamical systems. We test it on five Hamiltonian systems, including the gravitational 3-body problem, and find that it discovers not only all exactly conserved quantities, but also periodic orbits, phase transitions and breakdown timescales for approximate conservation laws.
