Course Description
Many significant reallife problems today are interdisciplinary in nature, involving physical, biological sciences,
and/or social sciences, mathematics, and computer science in an area called computational science. Computing and modeling can often
stimulate the insight and understanding that theory and experiment alone cannot achieve. This course prepares students to understand and
utilize fundamental concepts of computational science. The course considers two major modeling approaches: system dynamics models and
cellular automaton simulations. System dynamics models provide integral views of interacting system components that change with time.
To model such dynamic systems, students will employ a tool called Vensim. With this tool, students can create pictorial representations
of models, develop relationships, run simulations, and generate graphs of the results. In contrast to system dynamics, cellular automaton
simulations provide local views of individuals affecting individuals. The virtual world under consideration consists of a rectangular grid
of cells, and each cell has a state that can change with time according to certain rules.
This course does not require computer programming experience. The concept of rate of change is used throughout the course. The course
will provide the necessary background for the student to understand the material and confidently succeed in the course. The course covers
numerous applications in a wide variety of areas. Each class involving such an application will provide the prerequisite science without
overwhelming the students with excessive detail.
In Turkey, we will study the local issues, such as tourism, forest fire, fisheries, and ecology of Dalyan river and we will discuss
modeling of these problems.
Textbook:
Shiflet, A. B. and G. W. Shiflet, Introduction to Computational Science: Modeling and Simulation for the Sciences,
Princeton University Press, 2006.
Learning Outcomes

To be able to analyze the complex problems with a dynamic behavior as a system.
 The students can understand the complexity of problems presented in a textual form.
 The students can identify the components of a complex problem.
 The students can define the component action and interactions.

To be able to construct a model to represent a complex problem.
 The students can construct a simulation model to represent complex relationships of a system with variables, constraints, and formulas.
 The students can handle the random behavior of the components.
 The students can build computational models in the programming tool of the course.
 The students can rectify various types of computational errors in the models.
 The students can interpret the outcome of models and identify the role that the components play on these outcomes.

be able to understand complex problems in the immediate environment that can be tackled with the computational modeling.
 The students can describe the problem fully in writing.
 The students can identify the components of the problem and their actions and interactions.
 The students can form a rough model of the problem without data collection.

To be able to report on an application to a complex problem.
 The students can write a technical report on computational modeling of a complex problem.
 The students can present their findings to an audience in an effective manner.
Grading
it may be taken on a grade or a credit/nocredit basis.
The course grade will assigned on the basis of the following activities:
 Quizz/Tests 30%
 Projects 40%
 Final Exam 20%
 Presentations 10%
Course Outline (Tentative schedule)
Week 

1 
1

Overview of Computational Science
Programming tool: Vensim
Errors in computations
Rate of change
Model: Unconstrained Growth and Decay

2

Systems Dynamics Problems
Constrained Growth and Carrying capacity
Equilibrium and stability
Models: Drug dosage and Skydiving

3

Simulation Techniques
Eulerâ€™s method
RungeKutta 2 method

4

Models with interactions
Competition
Spread of SARS
Quiz 1


2 
5

Predator Prey Model
Introduction
LotkaVolterra Model
Projects

6

The Carbon Cycle
Flows between subsystems
Fossil fuels
Projects

7

Global warming
Greenhouse effect
Greenhouse gasses
Projects

8

Mercury pollution
Introduction
Projects
Quiz 2


3 
9

Cardiovascular Systems
Circulation
Blood pressure
Stroke volume
Blood flow
Projects

10

Managing Fishery
Economics background
GordonSchaefer Production function
Projects

11

Monte Carlo (MC) simulation
Random number generators
Generating events from simple distributions
Exercises

12

Calculating area through MC
Introduction
Exercises
Quiz 3


4 
13

Random walk
Introduction
Algorithm for Random walk
Average distance covered
Relationship between steps and distance

14

Spreading fire
Introduction
Updating rules
Simulation and Animation

15

Final Day
Project presentations
Final Exam

12

Calculating area through MC
Introduction
Exercises
Quiz 3

