# CSE 241 Computational Modeling (3 credits)

## Catalog description:

Introduction to computational modeling and simulation of physical, biological, and engineering problems through mathematics and computer science tools. Examples of problems studied are complex problems such as adjusting drug dosages, bungee jumping, enzyme kinetics, and controlling malaria. Students will develop computational models in a programming language such as Matlab.

## Prerequisite:

MTH 151 or permission of instructor

## Required Topics (approximate week allocation):

• Overview of Computational Science (2)
• Unconstrained Growth and Decay
• Constrained Growth Modeling
• Introduction to Drug Dosage (1)
• Introduction to Competition and Spread of SARS (1)
• Computational Errors (1)
• Euler's Method (1)
• Runge-Kutta 2 Method and brief mention of Runge-Kutta 4 Method (1)
• Computational Toolbox Matlab (1)
• Functions and Empirical Models (1)
• Representation of Randomness in models (1)
• "Multiplicative Linear Congruential Method"
• "Different Ranges of Random Numbers"
• Random Walk and simple Diffusion problems (1)
• Cellular Automaton
• Discrete Event Simulation (1)
• simulation of a simple queuing system by hand
• “Inventory control” simulation of a single item inventory system by hand (1)
• Review and test (1)
• Report and present the class projects (1)

## Course Outcomes

1: To be able to analyze complex science and engineering problems with a dynamic behavior as a system

1.1: The student can analyze complex problems presented in a text form

1.2: The student can identify the interacting components of a complex problem

1.3: The student can define the component action and interactions

2: To be able to construct a mathematical model to represent a complex problem

2.1: The students can construct a simulation model to represent dynamic behavior of a complex system as mathematical relationships with variables, constraints, and formulas

2.2: The students can build simulation models in the computer programming tools used in the class

2.3: The student can interpret the outcome of simulation models and identify the role that the components play on these outcomes

2.4: The student can handle the random behavior of the components

2.5: The students can rectify various types of computational errors in the models

3: To be able write a technical report for an application to a complex problem and present it to an audience

3.1: The student can write a technical report on computational modeling applied to a complex problem

3.2: The student can present their findings to an audience in an effective manner

4: To be able describe the difference between continuous and discrete-event simulations

4.1: The student can define the discrete-event simulation concepts (simulation clock, events, future event list, state, and statistics collection)

4.2: The students can simulate simple queuing problems manually