Identifier

etd-04262011-114601

Degree

Master of Science in Computer Science (MSCS)

Department

Computer Science

Document Type

Thesis

Abstract

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.

Date

2011

Document Availability at the Time of Submission

Release the entire work immediately for access worldwide.

Committee Chair

Allen, Gabrielle

DOI

10.31390/gradschool_theses.3058

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