Introductory Courses in Computational Models in Engineering

1Students learn, by considering examples, the concept of modeling as well as a taxonomy of modeling to include traditional and contemporary, normative and evaluative, linear and non-linear, and discrete and continuous models and systems.Dr. Stanfield
2Students collaborate on interactive exercises to take a problem, identify input (decision) and output (objective) parameters and develop a high level pictorial representation of their relationship. (Research Project 1 at end of session)Dr. Stanfield
3Students create a simple linear normative model, construct an associated
graphical representation and solution, and then review and run a
computational solution (in MATLAB). As an example of an evaluative
model, students run a discrete event simulation and compare its results to
analytical queuing methods.
Dr. Stanfield
4Students work in three groups to consider three modeling methods which
are biologically inspired and computationally implemented: genetic
algorithms and ant colony optimization (normative models) as well as
neural networks (evaluative). Pre-constructed solutions in MATLAB are
reviewed and run. Student teams explain their modeling method to the
remainder of the cohort. (Research Project 2 at end of session)
Dr. Stanfield
5Students work in pairs to determine modeling needs for computer solution
of their research problems. A synopsis of needed functionality is created
as a motivator for the subsequent MATLAB portion of the class.
Dr. Stanfield
6MATLAB fundamentals including MATLAB environment; arrays, vectors
and matrices; mathematical operations; built-in functions; plotting.
(Research Project 3 at end of session)
Dr. Kabadi
7Programming with MATLAB including script file; functions and
subfunctions; input-output; if structure; loops.
Dr. Kabadi
8MATLAB functions for numerical computations of roots of equations,
linear least-square and non-linear regressions, solution of large systems of
linear simultaneous equations, integration, and solution of systems of
ordinary differential equations. (Research Project 4 at end of session)
Dr. Kabadi
9Case studies of solutions of engineering problems with MATLABDr. Kabadi
10Introduction to Simulink; case study of modeling and simulation with
Simulink. (Research Project 5 at end of session)
Dr. Kabadi
11Computational Environment: Overview of computers and computational
systems, the Linux operating system - for computational modeling
Dr. Flurchick
12Computational Environment: The Linux operating system - for
computational modeling (continued), Modeling and Finite precision
arithmetic. (Research Project 6 at end of session)
Dr. Flurchick
13Generating Results and Analysis of Results: Models to algorithms and
Dr. Flurchick
14Generating Results and Analysis of Results: Scientific visualization
displays and tools, Visualization and analysis (Research Project 7 at end of
Dr. Flurchick
15Putting it all together: How do atoms make molecules - an exampleDr. Flurchick

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