Title page for ETD etd-04262011-114601

Type of Document Master's Thesis
Author Jiang, Lei
Author's Email Address lionelchange@gmail.com
URN etd-04262011-114601
Title Parallel Surrogate Detection in Large-Scale Simulations
Degree Master of Science in Systems Science (M.S.S.S.)
Department Computer Science
Advisory Committee
Advisor Name Title
Allen, Gabrielle Committee Chair
Chen, Qin Committee Member
Zhang, Jian Committee Member
  • large-scale simulations
  • data mining
  • scalability
  • surrogate modeling
  • automated workflow
Date of Defense 2011-04-08
Availability unrestricted
Simulation has become a useful approach in scientific computing and engineering for its ability to model real natural or human systems. In particular, for complex systems such as hurricanes, wildfire disasters, and real-time road traffic, simulation methods are able to provide researchers, engineers and decision makers predicted values in order to help them to take appropriate actions. For large-scale problems, the simulations usually take a lot of time on supercomputers, thus making real-time predictions more difficult. Approximation models that mimic the behavior of simulation models but are computationally cheaper, namely "surrogate models", are desired in such scenarios. In the thesis, a framework for scalable surrogate detection in large-scale simulations is presented with the basic idea of "using functions to represent functions". The following issues are discussed in the thesis: i) the data mining approaches to detecting and optimizing the surrogate models; ii) the scalable and automated workflow of constructing surrogate models from large-scale simulations; and iii) the system design and implementation with the application of storm surge simulations in the occurrence of hurricanes.
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