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. / Ph. D. / Uncertainties are ubiquitous in computer simulation models especially for network-based models where the underlying mechanisms are difficult to characterize explicitly by mathematical formalizations. Quantifying uncertainties is challenging because of either the lack of knowledge or their inherent indeterminate properties. Verification and validation of models with uncertainties cannot include every detail of real systems and therefore will remain a fundamental task in modeling. Many tools are developed for supporting uncertainty quantification, sensitivity analysis, and experimental design. However, few of them is domain-independent or supports the data management and complex simulation workflow of network-based simulation models.
In this dissertation, we present a computational framework called GENEUS, which incorporates a multitude of functions including uncertain parameter specification, experimental design, model execution management, data access and registrations, sensitivity analysis, surrogate modeling, and model calibration. 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 for various scenarios. GENEUS provides researchers access to uncertainty quantification, sensitivity analysis and experimental design methods with robust and tested implementations without requiring detailed expertise in modeling, statistics, or computing. Even for groups having all the skills, GENEUS can help save time, guard against mistakes and improve productivity.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/78764 |
| Date | 29 August 2017 |
| Creators | Wu, Sichao |
| Contributors | Computer Science, Marathe, Madhav Vishnu, Mortveit, Henning S., Cao, Yang, Deng, Xinwei, Ravi, Sekharipuram Subramaniam, Vullikanti, Anil Kumar S. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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