论文标题
运动网络常数
Constants of motion network
论文作者
论文摘要
物理学的美在于，通常在变化的系统（称为运动常数）中有保守的数量。找到运动的常数对于理解系统的动力学很重要，但通常需要数学水平和手动分析工作。在本文中，我们提出了一个神经网络，该网络可以同时学习系统的动力学和数据的常数。通过利用发现的运动常数，它可以对动态产生更好的预测，并且可以比基于汉密尔顿的神经网络在更广泛的系统上工作。此外，我们方法的训练进展可以用作系统中运动常数的指示，该系统可能在研究新型物理系统中有用。
The beauty of physics is that there is usually a conserved quantity in an always-changing system, known as the constant of motion. Finding the constant of motion is important in understanding the dynamics of the system, but typically requires mathematical proficiency and manual analytical work. In this paper, we present a neural network that can simultaneously learn the dynamics of the system and the constants of motion from data. By exploiting the discovered constants of motion, it can produce better predictions on dynamics and can work on a wider range of systems than Hamiltonian-based neural networks. In addition, the training progresses of our method can be used as an indication of the number of constants of motion in a system which could be useful in studying a novel physical system.
