典型文献
Exploring fundamental laws of classical mechanics via predicting the orbits of planets based on neural networks
文献摘要：
Neural networks have provided powerful approaches to solve various scientific problems.Many of them are even difficult for human experts who are good at accessing the physical laws from experimental data.We investigate whether neural networks can assist us in exploring the fundamental laws of classical mechanics from data of planetary motion.Firstly,we predict the orbits of planets in the geocentric system using the gate recurrent unit,one of the common neural networks.We find that the precision of the prediction is obviously improved when the information of the Sun is included in the training set.This result implies that the Sun is particularly important in the geocentric system without any prior knowledge,which inspires us to gain Copernicus'heliocentric theory.Secondly,we turn to the heliocentric system and make successfully mutual predictions between the position and velocity of planets.We hold that the successful prediction is due to the existence of enough conserved quantities(such as conservations of mechanical energy and angular momentum)in the system.Our research provides a new way to explore the existence of conserved quantities in mechanics system based on neural networks.
文献关键词：
中图分类号：
作者姓名：
Jian Zhang;Yiming Liu;Zhanchun Tu
作者机构：
Department of Physics,Beijing Normal University,Beijing 100875,China
文献出处：
引用格式：
[1]Jian Zhang;Yiming Liu;Zhanchun Tu-.Exploring fundamental laws of classical mechanics via predicting the orbits of planets based on neural networks)[J].中国物理B（英文版）,2022(09):417-421
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0.591404
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