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Browsing by Author "Wu, Sichao"

Now showing 1 - 3 of 3
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    CINET GDS-Calculator: Graph Dynamical Systems Visualization
    Wu, Sichao; Zhang, Yao (2012-12-10)
    This report summarizes the project of Graph Dynamical Systems Visualization, which is a subproject under the umbrella of project CINET. Base on some input information, we extract the character of system dynamics and output corresponding diagrams and charts that reflect the dynamical properties of the system, so that it can provide an intuitive and easy way for the researchers to analyze the GDSs. In the introduction section, some background information about the graph dynamical systems and their applications are given in the introduction. Then, we present the requirement analysis, including the task of the project, as well as the challenge we met. Next, this report records the system developing process, including the workflow, user’s manual, developer’ manual, etc. Finally, we summarize the future work. This report can serve as a combination of user’s manual, developer’s manual, and system manual.
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    Computational Framework for Uncertainty Quantification, Sensitivity Analysis and Experimental Design of Network-based Computer Simulation Models
    Wu, Sichao (Virginia Tech, 2017-08-29)
    When capturing a real-world, networked system using a simulation model, features are usually omitted or represented by probability distributions. Verification and validation (V and V) of such models is an inherent and fundamental challenge. Central to V and V, but also to model analysis and prediction, are uncertainty quantification (UQ), sensitivity analysis (SA) and design of experiments (DOE). In addition, network-based computer simulation models, as compared with models based on ordinary and partial differential equations (ODE and PDE), typically involve a significantly larger volume of more complex data. Efficient use of such models is challenging since it requires a broad set of skills ranging from domain expertise to in-depth knowledge including modeling, programming, algorithmics, high- performance computing, statistical analysis, and optimization. On top of this, the need to support reproducible experiments necessitates complete data tracking and management. Finally, the lack of standardization of simulation model configuration formats presents an extra challenge when developing technology intended to work across models. While there are tools and frameworks that address parts of the challenges above, to the best of our knowledge, none of them accomplishes all this in a model-independent and scientifically reproducible manner. In this dissertation, we present a computational framework called GENEUS that addresses these challenges. Specifically, it incorporates (i) a standardized model configuration format, (ii) a data flow management system with digital library functions helping to ensure scientific reproducibility, and (iii) a model-independent, expandable plugin-type library for efficiently conducting UQ/SA/DOE for network-based simulation models. This framework has been applied to systems ranging from fundamental graph dynamical systems (GDSs) to large-scale socio-technical simulation models with a broad range of analyses such as UQ and parameter studies for various scenarios. Graph dynamical systems provide a theoretical framework for network-based simulation models and have been studied theoretically in this dissertation. This includes a broad range of stability and sensitivity analyses offering insights into how GDSs respond to perturbations of their key components. This stability-focused, structure-to-function theory was a motivator for the design and implementation of GENEUS. GENEUS, rooted in the framework of GDS, provides modelers, experimentalists, and research groups access to a variety of UQ/SA/DOE methods with robust and tested implementations without requiring them to necessarily have the detailed expertise in statistics, data management and computing. Even for research teams having all the skills, GENEUS can significantly increase research productivity.
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    Relevance Feedback and Query Expansion
    Wu, Sichao; Zhang, Yao (2012-10-17)
    This module introduces the methods to improve the recall of information retrieval systems, mainly focuses on relevance feedback and query expansion.