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Journal article
Learning Order Parameters from Videos of Skyrmion Dynamical Phases with Neural Networks
PHYSICAL REVIEW APPLIED, v 16(1), 14005
02 Jul 2021
Abstract
The ability to recognize dynamical processes in physical events and to abstract physical concepts or even to reveal physical laws, lies at the core of human cognitive development. Modern machine learning methods have been given high hopes for realizing such ability, but are still in their infancy. Here we present our progress in this capability. The main purposes of this paper are to use neural networks for classifying the dynamical skyrmion phases of videos and to demonstrate that neural networks can learn physical concepts from the dynamical process. 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 that 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. Hence, 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 meaningful physical concepts and revealing unknown yet physical laws from videos.
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Details
- Title
- Learning Order Parameters from Videos of Skyrmion Dynamical Phases with Neural Networks
- Publication Details
- PHYSICAL REVIEW APPLIED, v 16(1), 14005
- Publisher
- AMER PHYSICAL SOC; COLLEGE PK
- Grant note
- We acknowledge Wenan Guo, Xintian Wu, and Huaiyang Yuan for helpful discussions about model simulations, and thank Lei Wang and Ka Shen for reading the manuscript and helpful suggestions. Also, we would like to thank Yuanyuan Mi, Zhuo Hao, and Penghao Rao for technical assistance regarding machine learning. K.X. is supported by the NSFC (Grants No. 61774017, No. 11734004, and No. 12088101) and NSAF (Grant No. U1930402) . We also acknowledge the National Supercomputer Center in Guangzhou (Tianhe II) for providing the computing resources. The authors used Keras [51] for machine learning.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Drexel University
- Web of Science ID
- WOS:000671573800004
- Scopus ID
- 2-s2.0-85109264038
- Other Identifier
- 991021860758204721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Physics, Applied