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Spatial distribution order parameter prediction of collective system using graph network |
| Huimin Zhao(赵慧敏)1, Rui Wang(王瑞)1, Cai Zhao(赵偲)2, and Wen Zheng(郑文)1,3,† |
1 Institute of Public-Safety and Big Data, College of Data Science, Taiyuan University of Technology, Taiyuan 030024, China;
2 Center of Information Management and Development, Taiyuan University of Technology, Taiyuan 030024, China;
3 Center for Healthy Big Data, Changzhi Medical College, Changzhi 046000, China |
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Abstract In the past few decades, the study of collective motion phase transition process has made great progress. It is also important for the description of the spatial distribution of particles. In this work, we propose a new order parameter φ to quantify the degree of order in the spatial distribution of particles. The results show that the spatial distribution order parameter can effectively describe the transition from a disorderly moving phase to a phase with a coherent motion of the particle distribution and the same conclusion could be obtained for systems with different sizes. Furthermore, we develop a powerful molecular dynamic graph network (MDGNet) model to realize the long-term prediction of the self-propelled collective system solely from the initial particle positions and movement angles. Employing this model, we successfully predict the order parameters of the specified time step. And the model can also be applied to analyze other types of complex systems with local interactions.
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Received: 06 October 2022
Revised: 05 December 2022
Accepted manuscript online: 08 February 2023
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PACS:
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64.75.-g
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(Phase equilibria)
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07.05.Mh
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(Neural networks, fuzzy logic, artificial intelligence)
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05.65.+b
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(Self-organized systems)
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87.64.Aa
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(Computer simulation)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant No. 11702289), Key core technology and generic technology research and development project of Shanxi Province of China (Grant No. 2020XXX013), and the National Key Research and Development Project of China. |
Corresponding Authors:
Wen Zheng
E-mail: zhengwen@tyut.edu.cn
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Cite this article:
Huimin Zhao(赵慧敏), Rui Wang(王瑞), Cai Zhao(赵偲), and Wen Zheng(郑文) Spatial distribution order parameter prediction of collective system using graph network 2023 Chin. Phys. B 32 056402
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[1] Köpf M H and Pismen L M 2013 Soft Matter 9 3727
[2] Soma R, Nakayama B, Kuwahara M, Yamamoto E and Saiki T 2020 Appl. Phys. Lett. 117 221601
[3] Keta Y E, Fodor é, van Wijland F, Cates M E and Jack R L 2021 Phys. Rev. E 103 022603
[4] Reichhardt C and Reichhardt C J O 2021 Phys. Rev. E 103 022602
[5] Cichos F, Gustavsson K, Mehlig B and Volpe G 2020 Nat. Mach. Intell. 2 94
[6] Zhang J, Zhao Y, Tian B, Peng L, Zhang H T, Wang B H and Zhou T 2009 Physica A 388 1237
[7] Zhang B and Shao Z G 2021 Physica A 563 125382
[8] Das M, Schmidt C F and Murrell M 2020 Soft Matter 16 7185
[9] Kumar S, Singh J P, Giri D and Mishra S 2021 Phys. Rev. E 104 024601
[10] Jadbabaie A, Lin J and Morse A S 2002 Proceedings of the 41st IEEE Conference on Decision and Control 3 2953
[11] Papadopoulou M, Hildenbrandt H, Sankey D W, Portugal S J and Hemelrijk C K 2022 PLoS Comput. Biol. 18 e1009772
[12] Hall B, Roggero A, Baroni A and Carlson J 2021 Phys. Rev. D 104 063009
[13] Sarma S D, Deng D L and Duan L 2019 Physics Today 72 48
[14] Vicsek T, Czirók A, Ben-Jacob E, Cohen I and Shochet O 1995 Phys. Rev. Lett. 75 1226
[15] Tian B M, Yang H X, Li W, Wang W X, Wang B H and Zhou T 2009 Phys. Rev. E 79 052102
[16] Mehta P, Bukov M, Wang C H, Day A G, Richardson C, Fisher C K and Schwab D J 2019 Phys. Rep. 810 1
[17] Schütt K T, Chmiela S, von Lilienfeld O A, Tkatchenko A, Tsuda K and Müller K R 2020 Lect. Notes Phys. 968 48
[18] Brunton S L, Noack B R and Koumoutsakos P 2020 Ann. Rev. Fluid Mech. 52 477
[19] Carleo G, Cirac I, Cranmer K, Daudet L, Schuld M, Tishby N, Vogt-Maranto L and Zdeborová L 2019 Rev. Mod. Phys. 91 045002
[20] Bourilkov D 2019 International Journal of Modern Physics A 34 1930019
[21] Zitnik M, Agrawal M and Leskovec J 2018 Bioinformatics 34 i457
[22] Papadopoulou M, Hildenbrandt H, Sankey D W, Portugal S J and Hemelrijk C K 2022 PLoS Computational Biology 18 e100977
[23] Zhang T, Song A and Lan Y 2020 Scientia Sinica Informationis 50 347
[24] Brunton S L, Noack B R and Koumoutsakos P 2020 Annual Review of Fluid Mechanics 52 477
[25] Shlomi J, Battaglia P and Vlimant J R 2020 Machine Learning: Science and Technology 2 021001
[26] Jiang W and Luo J 2022 Exp. Sys. Appl. 207 117921
[27] Zöttl A 2020 Chin. Phys. B 29 074701
[28] Tsitsulin A, Palowitch J, Perozzi B and Müller E 2020 arXiv: 2006.16904
[29] Cao S, Sun X, Bo L, Wei Y and Li B 2021 Inform. Software Technol. 136 106576
[30] Ulices Q S, Pedro E R G and Alexis T C 2021 Soft Matter 17 1975
[31] Pata J, Duarte J, Vlimant J R, Pierini M and Spiropulu M 2021 Eur. Phys. J. C 81 1
[32] Zhang J, Lei Y K, Zhang Z, Chang J, Li M, Han X, Yang L, Yang Y I and Gao Y Q 2020 J. Phys. Chem. A 124 6745
[33] Battaglia P W, Pascanu R, Lai M, Rezende D and Kavukcuoglu K 2016 arXiv: 1612.00222
[34] Dulaney A R and Brady J F 2021 Soft Matter 17 6808
[35] Wang R, Fang F, Cui J and Zheng W 2022 Sci. Rep. 12 1
[36] Hachijo T, Gotoda H, Nishizawa T and Kazawa J 2020 J. Appl. Phys. 127 234901
[37] Ganaie M, Ghosh S, Mendola N, Tanveer M and Jalan S 2020 Chaos 30 063128
[38] Bhaskar D, Manhart A, Milzman J, Nardini J T, Storey K M, Topaz C M and Ziegelmeier L 2019 Chaos 29 123125
[39] Battaglia P W, Hamrick J B, Bapst V, et al. 2018 arXiv: 1806. 01261
[40] Bapst V, Keck T, Grabska-Barwińska A, Donner C, Cubuk E D, Schoenholz S S, Obika A, Nelson A W, Back T, Hassabis D, et al. 2020 Nat. Phys. 16 448
[41] Li Z and Farimani A B 2022 Computers & Graphics 103 201
[42] Deng L, Zhao C, Xu Z and Zheng W 2020 Eur. Phys. J. E 43 1
[43] Zhang H T, Chen M Z, Zhou T, Cheng Z and Yu P Z 2009 Complex Sci. 5 2159 |
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