Math 4380/6382 - Modeling and Simulation (Lenin). This course focuses on probabilistic modeling and discrete event simulations, such as traffic modeling, customer service wait times, and allocation of personnel resources such as bank tellers and grocery store cashiers. Topics include properties of random numbers, generating non-uniform random numbers, probabilistic models such as queuing models, and various discrete event simulations, using a parallel software package called OMNeT++. The students can use the cluster to simulate more complicated systems than are possible on single computers.
Spatial Modeling (Carmack). This course in spatial statistics has applications in a variety of disciplines, including epidemiology, oil and gas exploration, and atmospheric modeling. The absence of a computing cluster constrains in-class examples and the scope of student projects. With the availability of a cluster, Carmack will be able to include more realistic and sophisticated real-time examples in class and the students will be able to undertake more complicated projects outside class, providing them with hands-on experience and computing resources comparable with what they will encounter in industry.
Data Mining (Carmack). This course in data mining focuses on algorithms for manipulating and analyzing very large data sets. A course in data mining is generally infeasible without access to a computing cluster to handle both large data sets and the computational intensive algorithms. Students will have access to real-time applications of data mining both in the classroom and for student projects. This course will prepare the students for future careers at companies such as Acxiom where demand for capable data analysts is high.
Introduction to Computational Fluid Dynamics (Burg). This course focuses on the steps involved in the computational simulation of fluid dynamics, such as air flow past wings, water flow around submarines and the simulation of hurricanes. These steps include the construction of a digital geometry, generation of a computational mesh, simulation of the physical system for pressure, velocity and density and the visualization of the results. Students will learn how to use different computational tools for each step of the process and use these tools for one or more individual projects, hopefully being well aware of the limitations and pitfalls of these tools.