Machine-learning-assisted construction of appropriate rotating frame

Autor:	Michishita, Yoshihiro
Rok vydání:	2022
Předmět:	Condensed Matter - Disordered Systems and Neural Networks Quantum Physics
Druh dokumentu:	Working Paper
Popis:	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. Comment: 6 pages and 4 figures in the main text, and 5 pages and 8 figures in supplemental materials
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.15269 Zobrazit plný text záznamu View this record from Arxiv