scholarly journals Strategic Use of Data Assimilation for Dynamic Data-Driven Simulation

2020 ◽  
pp. 31-44
Author(s):  
Yubin Cho ◽  
Yilin Huang ◽  
Alexander Verbraeck
Keyword(s):  
Data Assimilation ◽  
Data Driven ◽  
Dynamic Data ◽  
Use Of Data

scholarly journals A dynamic data-driven method for dealing with model structural error in soil moisture data assimilation

2019 ◽  
Vol 132 ◽  
pp. 103407 ◽  
Author(s):  
Qiuru Zhang ◽  
Liangsheng Shi ◽  
Mauro Holzman ◽  
Ming Ye ◽  
Yakun Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Data Driven ◽  
Dynamic Data ◽  
Soil Moisture Data ◽  
Structural Error

scholarly journals Mixture Ensembles for Data Assimilation in Dynamic Data-driven Environmental Systems1

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

Dynamic Data-Driven Adaptive Observations in Data Assimilation for Multi-scale Systems

2018 ◽  
pp. 47-73 ◽  
Author(s):  
Hoong C. Yeong ◽  
Ryne Beeson ◽  
N. Sri Namachchivaya ◽  
Nicolas Perkowski ◽  
Peter W. Sauer
Keyword(s):  
Data Assimilation ◽  
Data Driven ◽  
Dynamic Data ◽  
Multi Scale ◽  
Adaptive Observations

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

2018 ◽  
Author(s):  
Peng Wang ◽  
Ge Li ◽  
Rusheng Ju ◽  
Xiang Zhang ◽  
Kedi Huang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Data Driven ◽  
Dynamic Data ◽  
Random Finite Set ◽  
Finite Set

scholarly journals Random Finite Set Based Data Assimilation for Dynamic Data Driven Simulation of Maritime Pirate Activity

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Peng Wang ◽  
Ge Li ◽  
Rusheng Ju ◽  
Yong Peng
Keyword(s):  
Data Assimilation ◽  
Rapid Development ◽  
Measurement Model ◽  
Data Driven ◽  
Agent Based Simulation ◽  
Dynamic Data ◽  
Agent Based ◽  
Random Finite Set ◽  
Finite Set ◽  
Online Measurements

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.

Application of dynamic data driven application system in environmental science

2014 ◽  
Vol 22 (3) ◽  
pp. 287-297 ◽  
Author(s):  
Jingwei Song ◽  
Bo Xiang ◽  
Xinyuan Wang ◽  
Li Wu ◽  
Chun Chang
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Computational Models ◽  
Model Simulation ◽  
Spatial Scales ◽  
Data Driven ◽  
Time Data ◽  
Environmental Sciences ◽  
Dynamic Data ◽  
Application System

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.

scholarly journals On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1310
Author(s):  
Pablo Torres ◽  
Soledad Le Clainche ◽  
Ricardo Vinuesa
Keyword(s):  
Physical Phenomenon ◽  
Urban Pollution ◽  
Pollutant Dispersion ◽  
Urban Environments ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Thermal Fields ◽  
Use Of Data ◽  
Sustainability Challenges ◽  
New Research

Understanding the flow in urban environments is an increasingly relevant problem due to its significant impact on air quality and thermal effects in cities worldwide. In this review we provide an overview of efforts based on experiments and simulations to gain insight into this complex physical phenomenon. We highlight the relevance of coherent structures in urban flows, which are responsible for the pollutant-dispersion and thermal fields in the city. We also suggest a more widespread use of data-driven methods to characterize flow structures as a way to further understand the dynamics of urban flows, with the aim of tackling the important sustainability challenges associated with them. Artificial intelligence and urban flows should be combined into a new research line, where classical data-driven tools and machine-learning algorithms can shed light on the physical mechanisms associated with urban pollution.

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