Exploring fundamental laws of classical mechanics via predicting the orbits of planets based on neural networks

TL;DR

This paper demonstrates that neural networks can predict planetary orbits and uncover fundamental laws of classical mechanics, such as conservation laws, by analyzing planetary motion data.

Contribution

It shows neural networks can be used to explore and identify fundamental physical laws and conserved quantities in classical mechanics from planetary data.

Findings

Including the Sun improves orbit prediction accuracy.

Mutual prediction of position and velocity indicates conserved quantities.

Neural networks can help discover physical laws from data.

Abstract

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.