Learning Order Parameters from Videos of Dynamical Phases for Skyrmions with Neural Networks

TL;DR

This paper demonstrates that neural networks can classify dynamical phases of skyrmions from videos, learn order parameters, and predict phase boundaries, aiding the discovery of physical concepts from visual data.

Contribution

It introduces a neural network-based method to identify dynamical phases, learn order parameters, and interpret physical laws from videos of skyrmion systems.

Findings

Neural networks accurately classify static and dynamical phases.

Networks predict phase boundaries consistent with simulations.

Two order parameters suffice to identify all phases.

Abstract

The ability to recognize dynamical phenomena (e.g., dynamical phases) and dynamical processes in physical events from videos, then to abstract physical concepts and reveal physical laws, lies at the core of human intelligence. The main purposes of this paper are to use neural networks for classifying the dynamical phases of some videos and to demonstrate that neural networks can learn physical concepts from them. To this end, we employ multiple neural networks to recognize the static phases (image format) and dynamical phases (video format) of a particle-based skyrmion model. Our results show that neural networks, without any prior knowledge, can not only correctly classify these phases, but also predict the phase boundaries which agree with those obtained by simulation. We further propose a parameter visualization scheme to interpret what neural networks have learned. We show that neural networks can learn two order parameters from videos of dynamical phases and predict the critical values of two order parameters. Finally, we demonstrate that only two order parameters are needed to identify videos of skyrmion dynamical phases. It shows that this parameter visualization scheme can be used to determine how many order parameters are needed to fully recognize the input phases. Our work sheds light on the future use of neural networks in discovering new physical concepts and revealing unknown yet physical laws from videos.