Mike Weisman received a BE in Electrical Engineering from the Cooper Union for the Advancement of Science and Art and a PhD in Applied Mathematics from Harvard University. He has previously been a member of the Senior Technical Staff at the JHU Applied Physics Laboratory and Technical Staff at MIT Lincoln Laboratory. Dr. Weisman is presently employed as a mathematician in the Computational and Information Sciences Directorate at the US Army Research Laboratory.
Current course offerings:
Spring '19: 605.716 - Modeling and Simulation of Complex Systems (CS),
New!: This course (605.716) will now be co-taught with Dr. Dan Wiley. We are updating the course materials! This is a multi-disciplinary course, appropriate for students in any of the Engineering for Professionals tracks.
Modeling and Simulation of Complex Systems is now featured in the
[At the Computer Science Home Page, a link to the Course Spotlight video is on the left.]
Summer '18: 625.703 - Functions of a Complex Variable (ACM)
Fall '18: 625.740 - Data Mining (ACM)
Daniel Wiley holds a BS degrees in Mathematics and Physics from Portland State University and a PhD in Applied Mathematics from Cornell University. He specialized in complex systems and worked for two summers as an instructor at the Mathematical and Theoretical Biology Institute. Dr. Wiley held academic positions at Howard University, the Mathematical Sciences Research Institute, and the University of Maryland, College Park. Dr. Wiley currently reviews potential publications for the journal CHAOS. He is presently employed as a mathematician for the U.S. Government.
This multi-disciplinary course focuses on the application of modeling and simulation principles to complex systems. A complex system is a large-scale nonlinear system consisting of interconnected or interwoven parts (such as a biological organism, an ecological system, the economy, fluids or strongly-coupled solids). The subject is interdisciplinary with foundations in mathematics, nonlinear science, numerical simulations and statistical physics. The course begins with an overview of complex systems, followed by modeling techniques based on nonlinear differential equations, networks, and stochastic models. Simulations are conducted via numerical calculus, analog circuits, Monte Carlo methods, and cellular automata. In the course we will model, program, and analyze a wide variety of complex systems, including dynamical and chaotic systems, cellular automata, and iterated functions. By defining and iterating an individual course project throughout the term, students will gain hands-on experience and understanding of complex systems that arise from combinations of elementary rules. Students will be able to define, solve, and plot systems of linear and non-linear systems of differential equations and model various complex systems important in applications of population biology, epidemiology, circuit theory, fluid mechanics, and statistical physics.
Knowledge of elementary probability and statistics and previous exposure to differential equations. Students applying this course to the MS in Bioinformatics should also have completed at least one Bioinformatics course prior to enrollment.
In this course, we will model, program, and analyze a wide variety of complex systems, including dynamical and chaotic systems, cellular automata, fractals, and iterated functions.
We will use Mathematica and Logo to code our simulations. Students will also do a project on a topic of interest, which may be coded in any language of the student's choice.
A portion of the class time will be dedicated to hands-on learning. There will be a number of laboratories where we will become familiar with basic circuit elements and then build a chaotic circuit!
Students wishing to obtain credit in Bioinformatics may choose a topic with biological content for the project.
- By defining and iterating a course project throughout the term, we will gain hands-on experience and understanding of complex systems that arise from combinations of elementary rules.
- Define, solve, and plot systems of linear and non-linear systems of differential equations,
- Understand and model various important complex systems (e.g. Lorenz Attractor, space filling curves, Brownian motion).
When This Course is Typically Offered
This course is typically offered in the spring at APL.
- phase portraits
- non-linear dynamical systems
- fractals and space filling curves
- brownian motion
- cellular automata
Student Assessment Criteria
Students will have opportunities to work hands-on with circuit elements to become familiar with the components and build a chaotic circuit! We will invest some class time in proposing and discussing class projects that hopefully will be in line with students' interests and will be fun and interesting!
Computer and Technical Requirements
Students are expected to have some experience in computer programming. We will explore and experiment with the Logo and Mathematica programming languages and exhibit complex and interesting patterns obtained from a few simple rules. By modeling and building chaotic circuits, we will gain an appreciation for, and develop an understanding of the workings of Lorenz, Rikitake, and other attractors.
The course will be a balanced mix of theory, computation, and lab work. A portion of the class meetings will be dedicated to discussing and developing the course project.
Textbook information for this course is available online through the MBS Direct Virtual Bookstore.
There are no notes for this course.
Final Words from the Instructor
Modeling and Simulation of Complex Systems is now featured at
Computer Science Course Spotlight!
Term Specific Course Website
(Last Modified: 02/07/2019 11:56:44 PM)