论文标题
通过人工智能工具重建观察到的机械动作
Reconstruction of observed mechanical motions with Artificial Intelligence tools
论文作者
论文摘要
本文的目的是确定观察到的轨迹定律，假设背景中有机械系统，并使用这些定律以合理的方式继续观察到的运动。法律由参数数量有限的神经网络表示。网络的培训遵循极端的学习机构想法。我们确定不同级别嵌入的定律，因此我们不仅可以代表运动方程，还可以代表不同种类的对称性。在系统的递归数值演化中，我们需要在确定的数值精度内履行所有观察到的定律。通过这种方式，我们可以成功地重建可集成和混乱的运动，正如我们在重力摆和双摆的示例中所证明的那样。
The goal of this paper is to determine the laws of observed trajectories assuming that there is a mechanical system in the background and using these laws to continue the observed motion in a plausible way. The laws are represented by neural networks with a limited number of parameters. The training of the networks follows the Extreme Learning Machine idea. We determine laws for different levels of embedding, thus we can represent not only the equation of motion but also the symmetries of different kinds. In the recursive numerical evolution of the system, we require the fulfillment of all the observed laws, within the determined numerical precision. In this way, we can successfully reconstruct both integrable and chaotic motions, as we demonstrate in the example of the gravity pendulum and the double pendulum.
