Computational Modeling and Simulation
4040-849-03
Spring 2012-3/Wang
Course Syllabus
Instructor
Information
Prerequisites
Course Description
Linwei Wang
Email: linwei.wang@rit.edu
Office: 74-1075
Phone: 475-2438
Web: phd.gccis.rit.edu/linweiwang/
 Undergraduate calculus, linear algebra and probabiilty.
 Computer programming / matlab experience
 Or permission of instructor.
Everyone uses modeling and simulation even without being aware of it. This course talks
about mathematical and computational modeling and simulation as the tools to solve
complex problems in the real world. While scientists and engineers essentially try to
understand, develop, or optimize “complex systems”, computational and mathematical
models are natural tools to break up the complexity of a system and use simplified
descriptions of that system. Topics are divided by the category of modeling method:
phenomenological models vs. mechanistic models. For mechanistic models, the course will
cover differential equations (including variational principle to construct the differential
equations, solutions to ODEs, and classical ODE systems) and cellular automaton in detail,
and mention other mechanistic models. Similarly, for phenomenological models, the
course will cover regression and neural networks in detail, and introduce other
phenomenological models such as networks and power-law distributions. In parallel,
paper review and discussion will serve as case studies of modeling of real-world complex
systems, illustrating application domains. Course projects are required.
Course Outcomes
After taking this course, a student will be:
 Equipped with knowledge of the state-of-the-art methods for computational modeling and
simulation
 Able to apply the mathematics underlying the relevant techniques
 Able to develop computational implementations of these techniques in a variety of realword problems across different domains
Textbooks
No Required Textbook.
 Lecture notes will be distributed.
Optional Textbooks:
 Mathematical Modeling and Simulation: Introduction for Scientists and Engineers, Kai
Velten, Wiley-VCH
Other resources:
(readings from
research/industry)
Assessment and Grading
Component
Participation
Homework
Critical Paper Review
Projects
Weight Comments
10%
10%
20%
 Midterm presentation + report (25%)
60%  Progress report (5%)
 Final presentation + report (30%)
Course Schedule:
1. Principle of Mathematical Modeling: Systems, Models, and Simulations
2. Mechanistic Models
1. Differential equations
1. Variational principle
2. Solutions to ODE
3. Classic ODE systems
2. Cellular automaton
3. Other mechanistic models
3. Phenomenological Models
1. Regression
2. Neural networks
3. Other phenomenological models
4. Summary
Apart from homework and assignments for each lecture, each student will complete 1 course project. Each
student will choose a topic of their interest, preferably related to their research area. Each student will
propose, report the progress, and present the final results of the project along the 11-week schedule of the
course.
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At week 3, each student will submit a progress report to describe their idea of the project.
For midterm, each student will formally present the proposal for their project. Deliverables include a
technical report and presentation.
At week 8, each student will submit another 1-page progress report describing their current progress on
the project.
At week 11, each student will submit their project. Deliverables include implementation of the project,
results, technical report, and presentation.
Specification for the progress reports, project reports and presentation will be given for each task.