Dynamic Data Driven Simulation: An Overview

doi:10.16182/j.issn1004731x.joss.24-0127

Abstract

Abstract:

Dynamic data driven simulation is a simulation paradigm which integrates simulation and data together. This paradigm continuously feeds real-time data into the simulation, enabling the simulation be dynamically adjusted by the data, which thus improves the simulation-based estimation and prediction capability. Due to this integration, the dynamic data driven simulation can estimate system states and predict future state evolution more accurately. This paper reviews the origins and basic concept of dynamic data driven simulation, and introduces several simulation paradigms originated from the idea of "integrating models with data", and identifies the linkages and differences among them. The particle filter-based data assimilation method and the identical-twin simulation experiment are introduced. The current research status of dynamic data driven simulation is summarized from four perspectives, i.e., application scenarios, models and data, data assimilation algorithms, and integration with new technologies. Finally, the future research directions are outlooked from five aspects, which are simulation models, measurement data, data assimilation, algorithm performance, and application areas.

Key words: modeling and simulation, dynamic data driven simulation, data assimilation, particle filters, identical-twin simulation experiment

CLC Number:

TP391.9

Xie Xu, Qiu Xiaogang, Bao Yizheng, Xu Kai. Dynamic Data Driven Simulation: An Overview[J]. Journal of System Simulation, 2024, 36(8): 1832-1842.

Figures/Tables 3

References 58

1	Long Yuan, Hu Xiaolin. Dynamic Data Driven Simulation with Soft Data[C]//Proceedings of the Symposium on Theory of Modeling & Simulation-DEVS Integrative. San Diego: Society for Computer Simulation International, 2014: 16:1-16.
2	Sarwar Azeem. Spatiotemporal Systems: Gradual Variations, Identification, Adaptation and Robustness[D]. Champaign: University of Illinois Urbana-Champaign, 2009.
3	Lahoz William A, Schneider Philipp. Data Assimilation: Making Sense of Earth Observation[J]. Frontiers in Environmental Science, 2014, 2: 1-16.
4	Treiber Martin, Kesting Arne. Traffic Flow Dynamics: Data, Models and Simulation[M]. Berlin: Springer Berlin Heidelberg, 2013.
5	Leduc G. Road Traffic Data: Collection Methods and Applications: JRC47967[R]. [S.l.]: [s.n.], 2008: 1-52.
6	Bouttier F, Courtier P. Data Assimilation Concepts and Methods[R]. [S.l.]: [s.n.], 1999: 1-59.
7	Darema F. Dynamic Data Driven Applications Systems: A New Paradigm for Application Simulations and Measurements[C]//Computational Science-ICCS 2004. Berlin: Springer Berlin Heidelberg, 2004: 662-669.
8	Darema F. Dynamic Data Driven Applications Systems: New Capabilities for Application Simulations and Measurements[C]//Computational Science-ICCS 2005. Berlin: Springer Berlin Heidelberg, 2005: 610-615.
9	Hu X. Dynamic Data Driven Simulation[J]. SCS M & S Magazine II, 2011, 1: 16-22.
10	黄柯棣, 邱晓刚, 查亚兵, 等. 建模与仿真技术[M]. 长沙: 国防科技大学出版社, 2010.
	Huang Kedi, Qiu Xiaogang, Zha Yabing, et al. Modeling and Simulation Technology[M]. Changsha: National University of Defense Technology Press, 2010.
11	Zeigler B, Praehofer H, Kim T G. Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems[M]. 2nd ed. [S.l.]: Academic Press, 2000.
12	Xie X. Data Assimilation in Discrete Event Simulations[D]. Delft: Delft University of Technology, 2018.
13	Bai Fan, Guo Song, Hu Xiaolin. Towards Parameter Estimation in Wildfire Spread Simulation Based on Sequential Monte Carlo Methods[C]//Proceedings of the 44th Annual Simulation Symposium. San Diego: Society for Computer Simulation International, 2011: 159-166.
14	Zhang Bo, Zhong Jinghui, Cai Wentong. A Data-driven Approach for Pedestrian Intention Prediction in Large Public Places[C]//Proceedings of the 2022 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation. New York: Association for Computing Machinery, 2022: 33-36.
15	Huang Yilin, Seck M D, Verbraeck Alexander. Component-based Light-rail Modeling in Discrete Event Systems Specification DEVS[J]. Simulation, 2015, 91(12): 1027-1051.
16	Wang Junfeng, Chang Qing, Xiao Guoxian, et al. Data Driven Production Modeling and Simulation of Complex Automobile General Assembly Plant[J]. Computers in Industry, 2011, 62(7): 765-775.
17	Aydt H, Turner S J, Cai Wentong, et al. Symbiotic Simulation Systems: An Extended Definition Motivated by Symbiosis in Biology[C]//2008 22nd Workshop on Principles of Advanced and Distributed Simulation. Piscataway: IEEE, 2008: 109-116.
18	Kamrani Farzad, Ayani Rassul. Using On-line Simulation for Adaptive Path Planning of UAVs[C]//11th IEEE International Symposium on Distributed Simulation and Real-Time Applications (DS-RT'07). Piscataway: IEEE, 2007: 167-174.
19	段伟. 平行仿真的内涵、发展与应用[J]. 指挥与控制学报, 2019, 5(2): 82-86.
	Duan Wei. Parallel Simulation: Motivation, Concept and Application[J]. Journal of Command and Control, 2019, 5(2): 82-86.
20	王会霞. 平行仿真技术研究[J]. 航天控制, 2016, 34(6): 64-67.
	Wang Huixia. Research on Parallel Simulation Technology[J]. Aerospace Control, 2016, 34(6): 64-67.
21	窦林涛, 初阳, 周玉芳, 等. 平行仿真技术在指控系统中的应用构想[J]. 指挥控制与仿真, 2017, 39(1): 62-69.
	Dou Lintao, Chu Yang, Zhou Yufang, et al. Conception of the Application of Parallel Simulation Technology in Command and Control System[J]. Command Control & Simulation, 2017, 39(1): 62-69.
22	Grieves M, Vickers J. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems[M]//Franz-Josef Kahlen, Flumerfelt S, Anabela Alves. Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches. Cham: Springer International Publishing, 2017: 85-113.
23	张霖. 关于数字孪生的冷思考及其背后的建模和仿真技术[J]. 系统仿真学报, 2020, 32(4): 1-10.
	Zhang Lin. Cold Thinking about the Digital Twin and the Modeling and Simulation Techniques Behind It[J]. Journal of System Simulation, 2020, 32(4): 1-10.
24	Liu Mengnan, Fang Shuiliang, Dong Huiyue, et al. Review of Digital Twin About Concepts, Technologies, and Industrial Applications[J]. Journal of Manufacturing Systems, 2021, 58, Part B: 346-361.
25	杨林瑶, 陈思远, 王晓, 等. 数字孪生与平行系统: 发展现状、对比及展望[J]. 自动化学报, 2019, 45(11): 2001-2031.
	Yang Linyao, Chen Siyuan, Wang Xiao, et al. Digital Twins and Parallel Systems: State of the Art, Comparisons and Prospect[J]. Acta Automatica Sinica, 2019, 45(11): 2001-2031.
26	陶飞, 张贺, 戚庆林, 等. 数字孪生十问:分析与思考[J]. 计算机集成制造系统, 2020, 26(1): 1-17.
	Tao Fei, Zhang He, Qi Qinglin, et al. Ten Questions Towards Digital Twin: Analysis and Thinking[J]. Computer Integrated Manufacturing Systems, 2020, 26(1): 1-17.
27	Nichols N K. Data Assimilation: Aims and Basic Concepts[C]//Data Assimilation for the Earth System. Dordrecht: Springer Netherlands, 2003: 9-20.
28	Ide K, Courtier P, Ghil M, et al. Unified Notation for Data Assimilation : Operational, Sequential and Variational[J]. Journal of the Meteorological Society of Japan. Ser. II, 1997, 75(1B): 181-189.
29	Wu Wanshu, Purser R J, Parrish D F. Three-dimensional Variational Analysis with Spatially Inhomogeneous Covariances[J]. Monthly Weather Review, 2022, 130(12): 2905-2916.
30	Lorenc A C, Rawlins F. Why does 4D-var beat 3D-Var?[J]. Quarterly Journal of the Royal Meteorological Society, 2005, 131(613): 3247-3257.
31	Arulampalam M S, Maskell S, Gordon N, et al. A Tutorial on Particle Filters for Online Nonlinear/non-gaussian Bayesian Tracking[J]. IEEE Transactions on Signal Processing, 2002, 50(2): 174-188.
32	Gillijns S, Mendoza O B, Chandrasekar J, et al. What is the Ensemble Kalman Filter and How Well Does It Work?[C]//Proceedings of the 2006 American Control Conference. Piscataway: IEEE, 2006: 4448-4453.
33	Evensen Geir. The Ensemble Kalman Filter: Theoretical Formulation and Practical Implementation[J]. Ocean Dynamics, 2003, 53(4): 343-367.
34	Djuric P M, Kotecha J H, Zhang J, et al. Particle Filtering[J]. IEEE Signal Processing Magazine, 2003, 20(5): 19-38.
35	Peter Jan van Leeuwen. Particle Filtering in Geophysical Systems[J]. Monthly Weather Review, 2009, 137(12): 4089-4114.
36	Xie X, Verbraeck Alexander. A Particle Filter-based Data Assimilation Framework for Discrete Event Simulations[J]. Simulation, 2019, 95(11): 1027-1053.
37	Xue Haidong, Gu Feng, Hu Xiaolin. Data Assimilation Using Sequential Monte Carlo Methods in Wildfire Spread Simulation[J]. ACM Transactions on Modeling and Computer Simulation, 2012, 22(4): 23.
38	Hu Xiaolin. Dynamic Data-driven Simulation: Real-time Data for Dynamic System Analysis and Prediction[M]. Singapore: World Scientific, 2023.
39	Hu Xiaolin, Wu Peisheng. A Data Assimilation Framework for Discrete Event Simulations[J]. ACM Transactions on Modeling and Computer Simulation, 2019, 29(3): 17.
40	Ciuffo B, Punzo V, Montanino M. The Calibration of Traffic Simulation Models. Report on the Assessment of Different Goodness of Fit Measures and Optimization Algorithms. MULTITUDE Project-COST Action TU0903: EUR25188[R]. [S.l.]: European Commission-Joint Research Centre, 2012: 1-84.
41	Gu Feng. Dynamic Data Driven Application System for Wildfire Spread Simulation[D]. Atlanta: Georgia State University, 2010.
42	Ntaimo L, Hu Xiaolin, Sun Yi. DEVS-FIRE: Towards an Integrated Simulation Environment for Surface Wildfire Spread and Containment[J]. Simulation, 2008, 84(4): 137-155.
43	Hu Xiaolin, Sun Yi, Ntaimo L. DEVS-FIRE: Design and Application of Formal Discrete Event Wildfire Spread and Suppression Models[J]. Simulation, 2012, 88(3): 259-279.
44	Wang Minghao, Hu Xiaolin. Data Assimilation in Agent Based Simulation of Smart Environments Using Particle Filters[J]. Simulation Modelling Practice and Theory, 2015, 56: 36-54.
45	Yan Xuefeng, Gu Feng, Hu Xiaolin, et al. Dynamic Data Driven Event Reconstruction for Traffic Simulation Using Sequential Monte Carlo Methods[C]//2013 Winter Simulations Conference (WSC). Piscataway: IEEE, 2013: 2042-2053.
46	Xie X, van Lint Hans, Verbraeck Alexander. A Generic Data Assimilation Framework for Vehicle Trajectory Reconstruction on Signalized Urban Arterials Using Particle Filters[J]. Transportation Research Part C: Emerging Technologies, 2018, 92: 364-391.
47	Wang Song, Xie Xu, Ju Rusheng. A Mesoscopic Traffic Data Assimilation Framework for Vehicle Density Estimation on Urban Traffic Networks Based on Particle Filters[J]. Entropy, 2019, 21(4): 358.
48	Xue Haidong, Hu Xiaolin. An Effective Proposal Distribution for Sequential Monte Carlo Methods-based Wildfire Data Assimilation[C]//2013 Winter Simulations Conference (WSC). Piscataway: IEEE, 2013: 1938-1949.
49	Bolic M, Djuric P M, Hong Sangjin. Resampling Algorithms and Architectures for Distributed Particle Filters[J]. IEEE Transactions on Signal Processing, 2005, 53(7): 2442-2450.
50	Bai Fan, Gu Feng, Hu Xiaolin, et al. Particle Routing in Distributed Particle Filters for Large-scale Spatial Temporal Systems[J]. IEEE Transactions on Parallel and Distributed Systems, 2016, 27(2): 481-493.
51	Al-Fuqaha A, Guizani Mohsen, Mohammadi M, et al. Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications[J]. IEEE Communications Surveys & Tutorials, 2015, 17(4): 2347-2376.
52	Shi Weisong, Cao Jie, Zhang Quan, et al. Edge Computing: Vision and Challenges[J]. IEEE Internet of Things Journal, 2016, 3(5): 637-646.
53	Shi Weisong, Dustdar Schahram. The Promise of Edge Computing[J]. Computer, 2016, 49(5): 78-81.
54	Mao Yuyi, You Changsheng, Zhang Jun, et al. A Survey on Mobile Edge Computing: The Communication Perspective[J]. IEEE Communications Surveys & Tutorials, 2017, 19(4): 2322-2358.
55	Xie X, Xu K. Distributed Dynamic Data Driven Simulations: Basic Idea and an Illustration Example[C]//2023 IEEE/ACM 27th International Symposium on Distributed Simulation and Real Time Applications (DS-RT). Piscataway: IEEE, 2023: 105-108.
56	Blasch E, Ravela S, Aved A. Handbook of Dynamic Data Driven Applications Systems[M]. Cham: Springer International Publishing, 2018.
57	Ma Yuqing, Xie Xu, Chen Hailiang. MaMiH: A New Data Assimilation Framework Based on Macro-micro Hierarchical Simulation Model[C]//Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023). Bellingham: SPIE, 2023: 127912L.
58	Huang Yilin, Xie Xu, Cho Y, et al. Particle Filter-based Data Assimilation in Dynamic Data-driven Simulation: Sensitivity Analysis of Three Critical Experimental Conditions[J]. Simulation, 2023, 99(4): 403-415.

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