Random finite set based data assimilation algorithm for dynamic data driven simulation

Author(s):  
Peng Wang ◽  
Ge Li ◽  
Rusheng Ju ◽  
Xiang Zhang ◽  
Kedi Huang ◽  
...  
2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Peng Wang ◽  
Ge Li ◽  
Rusheng Ju ◽  
Yong Peng

Maritime piracy is posing a genuine threat to maritime transport. The main purpose of simulation is to predict the behaviors of many actual systems, and it has been successfully applied in many fields. But the application of simulation in the maritime domain is still scarce. The rapid development of network and measurement technologies brings about higher accuracy and better availability of online measurements. This makes the simulation paradigm named as dynamic data driven simulation increasingly popular. It can assimilate the online measurements into the running simulation models and ensure much more accurate prediction of the complex systems under study. In this paper, we study how to utilize the online measurements in the agent based simulation of the maritime pirate activity. A new random finite set based data assimilation algorithm is proposed to overcome the limitations of the conventional vectors based data assimilation algorithms. The random finite set based general data model, measurement model, and simulation model are introduced to support the proposed algorithm. The details of the proposed algorithm are presented in the context of agent based simulation of maritime pirate activity. Two groups of experiments are used to practically prove the effectiveness and superiority of the proposed algorithm.


2019 ◽  
Vol 132 ◽  
pp. 103407 ◽  
Author(s):  
Qiuru Zhang ◽  
Liangsheng Shi ◽  
Mauro Holzman ◽  
Ming Ye ◽  
Yakun Wang ◽  
...  

2014 ◽  
Vol 29 ◽  
pp. 1266-1276 ◽  
Author(s):  
Piyush Tagade ◽  
Hansjörg Seybold ◽  
Sai Ravela

Author(s):  
Hoong C. Yeong ◽  
Ryne Beeson ◽  
N. Sri Namachchivaya ◽  
Nicolas Perkowski ◽  
Peter W. Sauer

2014 ◽  
Vol 22 (3) ◽  
pp. 287-297 ◽  
Author(s):  
Jingwei Song ◽  
Bo Xiang ◽  
Xinyuan Wang ◽  
Li Wu ◽  
Chun Chang

The paradigm of dynamic data driven application system (DDDAS) has been proposed as a framework to analyze and predict the character and behavior of complex systems that influence computational models significantly. Its accuracy and efficiency lies in its ability to integrate observations on different temporal and spatial scales from real-time sensors, and in its measurement steering and controlling capabilities. Many problems in environmental sciences are nonlinear and complex, impossible to solve by using input/output sequence flows without feedback control. Nonlinear system efficiency depends on measurement control and steering, on-line data assimilation, and model selection with dynamic optimization. Compared with traditional methods, DDDAS possesses the capacity to overcome these limitations. This paper discusses DDDAS and classifies typical cases of its application in environmental sciences into three levels of paradigm. Short reviews of multi-model simulation and data assimilation are provided for practical use. Recent developments and future perspectives are reviewed. Future work may address determining automatically where, when, and how to acquire real-time data, and its integration with GIS, to improve efficiency and accuracy. User-generated content will find wide application in the future. Considering the differences between DDDAS and other data-driven methods in solving the same nonlinear complex system problems, a combination of nonlinear science and chaos theory is advocated.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Giacomo Baggio ◽  
Danielle S. Bassett ◽  
Fabio Pasqualetti

AbstractOur ability to manipulate the behavior of complex networks depends on the design of efficient control algorithms and, critically, on the availability of an accurate and tractable model of the network dynamics. While the design of control algorithms for network systems has seen notable advances in the past few years, knowledge of the network dynamics is a ubiquitous assumption that is difficult to satisfy in practice. In this paper we overcome this limitation, and develop a data-driven framework to control a complex network optimally and without any knowledge of the network dynamics. Our optimal controls are constructed using a finite set of data, where the unknown network is stimulated with arbitrary and possibly random inputs. Although our controls are provably correct for networks with linear dynamics, we also characterize their performance against noisy data and in the presence of nonlinear dynamics, as they arise in power grid and brain networks.


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