论文标题
适当旋转框架的机器学习辅助构造
Machine-learning-assisted construction of appropriate rotating frame
论文作者
论文摘要
使用神经网络的机器学习现在已成为用于各种任务的一种越来越强大的工具，例如自然语言处理，图像识别，赢得游戏，甚至在物理问题上。尽管有许多关于机器学习在数值计算和实验检测帮助的研究，但是对发现机器学习以找到分析方法的方法进行了很少的研究。在这封信中，我们提出了使用机器学习来找到分析方法的方法。我们证明，复发性神经网络可以通过将时间周期性的哈密顿量输入到神经网络中，并在定期驱动的系统中得出适当的旋转框架，从而可以``得出''floquet-magnus扩展。我们还认为，该方法也适用于在其他系统中找到其他理论框架。
Machine learning with neural networks is now becoming a more and more powerful tool for various tasks, such as natural language processing, image recognition, winning the game, and even for the issues of physics. Although there are many studies on the application of machine learning to numerical calculation and the assistance of experimental detection, the methods of applying machine learning to find the analytical method are poorly studied. In this letter, we propose methods to use machine learning to find the analytical methods. We demonstrate that the recurrent neural networks can ``derive'' the Floquet-Magnus expansion just by inputting the time-periodic Hamiltonian to the neural networks, and derive the appropriate rotating frame in the periodically-driven system. We also argue that this method is also applicable to finding other theoretical frameworks in other systems.
