EGR 301, Fall 2004
Modeling and Simulation of Natural and Engineered Systems

Class Time: TR 9:00 - 10:20, EGR 201

Prerequisites: Physics 210, corequisites Math 204, EGR 320

Text: Introduction to Dynamic Systems: Theory, Models & Applications, by David G. Luenberger; Wiley, 1979, ISBN 0-471-02594-1

Course Overview:
The objective of this course is to introduce students to the fundamental theory, mathematics and modeling tools necessary to analyze and simulate natural and engineered systems. The course includes three broad areas of modeling and analysis: that of stationary processes, linear dynamic systems and neural networks.

Topics include modeling time series with ARIMA models, applications of artificial neural networks, building state space models for dynamic systems, and performing sensitivity and stability analyses. Students will have the opportunity to apply these tools to model systems in many areas of engineering, including: mechanical, civil, environmental, computer, chemical and electrical engineering.

Simple examples of this type of system include systems with masses and springs (mechanical), systems with resistors and energy storage devices (electrical circuits with capacitance and inductance), heat transfer (thermal), capacitors and resistors (fluid systems), energy converting and signal converting devices (mixed systems). More advanced examples of systems that could be analyzed include earthquake ground motion, water and wastewater treatment, financial markets, pendulums, robotic arms, spacecraft, electric power systems, the sustainable use of natural resources, the human body and natural waterways, to mention only a few.

Objectives:
Through the material presented in this course, students will be able to

1. Recognize engineering applications for ARIMA models, input-output and state-space models for linear dynamic systems, and artificial neural networks.
2. Identify time series data in the world around them; describe the properties of the time series; fit an ARIMA model to the time series; and apply the model for forecasting and simulation.
3. Identify and define linear dynamic systems in the world around them. Model and predict the behavior of these systems with respect to the system inputs, system evolution and the system outputs.
4. Develop a basic understanding of the concepts of stability and feedback control for linear dynamic systems.
5. Construct, train, and test feed-forward, back-propagation artificial neural networks to learn relationships among variables.
6. Identify both the appropriate applications and the limitations of each model developed.
7. Learn and use modeling tools, including Matlab and associated toolboxes.
8. Present project results, including the problem formulation and analysis method selected, through oral and written formats.

Course Documents

• Syllabus (Including important grading and other course policies)
• Matlab Hints and Tutorials:
  • Matlab Basics
  • Matlab's own tutorial
  • Utah tutorial
  • MIT-Strang Linear Algebra Matlab Hints
  • MIT-Signals Matlab Tutorial
  • Smith CS370 tutorial

EGR 301 Schedule - Weekly Summary, Spring 2004