CS 295 Computer Simulation and Modeling
Alexey Voinov
Tuesday and Thursday 2.003.15 Votey 367
Prerequisites: Math 121 or Instructor Permission
This course is an introduction to simulation modeling of dynamic systems that will include theoretical studies and handson modeling workshops. It will familiarize participants with systems analysis and modeling with applications and case studies drawn primarily from ecology and economics. Several modeling software packages will be introduced, including Stella, Madonna, StarLogo, etc. We will consider different modeling strategies and learn how to formulate, build and analyze models. Investigation of alternative modeling software packages is encouraged.
The course will meet twice a week. We will start with an introduction to systems modeling concepts and modeling software, with several worked out examples. We will look into some of the main mathematical paradigms that are used in dynamic modeling. Participants will work through example exercises, read and report on models in the current literature, and build, calibrate and run their own models, either alone or in a small team. By the end of the course participants will be expected to produce a working model, preferably related to their research topic. Course grading will be: 30% exercises, 20% class presentations and participation, 50% project.
Course Schedule
Week

Tuesday

Thursday

1

Introduction: Course overview. What is a model? What is simulation? The modeling process.

Projects: Suggested topics. Group assignments.

2

Types of Models and Simulations: Static vs. discrete vs. continuous; algebraic equations vs. differential equations; spatially uniform vs. spatially explicit; empirical vs. process based.

Modeling principles, energy conservation, causality, positive, negative feedbacks, aggregation/disaggregation.

3

Modeling software: Stella, Madonna, Simulink, Simile. Downloads and installation. Main principles. Similarities and differences.

Comparing packages. Pros and cons of modeling software.

4

Model analysis: Sensitivity, calibration, validation.

Intermediate project presentations: project description and approach.

5

Ordinary Differential Equations: definition; initial value problems vs. boundary value problems

Model analysis: equilibrium, stability. Analytical vs. computer modeling.

6

Integration algorithms: Euler, RungeKutta, implicit vs. explicit.

Intermediate project presentations: conceptual model.

7

Review. Questions & Answers

Spatial modeling: Partial differential equations; boundary conditions. Cellular automata.

8

Town Meeting Day  no class.

Modeling of ecological systems: LotkaVolterra model and its modifications. Chaos, dissipative systems.

9

Modeling of economic systems: algebraic equations, inputoutput analysis.

Intermediate project presentations: model formalization.

10

Spring Break

11

Watershed modeling: Hydrologic and climatic models, routing algorithms, model integration.

Agent based modeling: StarLogo and Swarm.

12

Network analysis. Petri nets.

Introduction to SME. Modeling and GIS.

13

Uncertainty analysis: MonteCarlo simulation, sources of uncertainty.

Intermediate project presentations: model calibration.

14

Optimization: goal functions and conditions, control variables, methods of optimization.

Spatial optimization and calibration. Map comparisons.

15

Discrete Event Systems (DEVS): definition, models, queuing example, comparison with energybased systems.

Final project presentations

16

Final project presentations


