1. Modeling and Simulation: Fundamentals and Implementation
Introduction

2. Outline Modeling and Simulation Course Overview
What?
Why?
Uses
Taxonomy
Course Overview
Model Development Life Cycle

3. Modeling and Simulation
Definitions
Model
A (usually miniature) representation of something; an example for imitation or emulation [Mirriam-Webster dictionary]
A description of observed behavior, simplified by ignoring certain details. Models allow complex systems to be understood and their behavior predicted within the scope of the model, but may give incorrect descriptions and predictions for situations outside the realm of their intended use. [
Simulation
The imitative representation of the functioning of one system or process by means of the functioning of another [Mirriam-Webster dictionary]
A sham object [counterfeit] !!!

4. Why Simulate?
It may be too difficult, hazardous, or expensive to observe a real, operational system
Parts of the system may not be observable (e.g., internals of a silicon chip or biological system)
Uses of simulations
Analyze systems before they are built
Reduce number of design mistakes
Optimize design
Analyze operational systems
Create virtual environments for training, entertainment

5. Applications: System Analysis
“Classical” application of simulation
Telecommunication networks
Transportation systems
Electronic systems (e.g., microelectronics, computer systems)
Battlefield simulations (blue army vs. red army)
Ecological systems
Manufacturing systems
Logistics
Focus typically on planning, system design

6. Applications: On-Line Decision Aids
interactive simulation environment
analysts and
decision makers
live
data
feeds
forecasting tool
(fast simulation)
situation
database
Simulation tool is used for fast analysis of alternate courses of action in time critical situations
Initialize simulation from situation database
Faster-than-real-time execution to evaluate effect of decisions
Applications: air traffic control, battle management
Simulation results may be needed in only seconds
Here to discuss technology. The technology we are developing is realizing faster than real time interactive simulations. Current TISES simulation runs require a few hours, depending on the scenario. We’d like to see that reduced to minutes.
The specific benefits from this capability fall into two categories:
doing what you do now, better
doing things you cannot do now

7. Applications: Virtual Environments
Uses: training (e.g., military, medicine, emergency planning), entertainment
Simulations are often used in virtual environments to create dynamic computer generated entities
Adversaries and helpers in video games
Defense: Computer generated forces (CGF)
Automated forces
Semi-automated forces
Physical phenomena
Trajectory of projectiles
Buildings “blowing up”
Environmental effects on environment (e.g., rain washing out terrain)

8. A Few Example Applications
Earth magnetosphere: understand space weather
Wargaming: test strategies; training
Transportation systems: improved operations; urban planning
Parallel computer systems: developing scalable software
Computer communication
network: protocol design

9. Simulation Fundamentals
A computer simulation is a computer program that models the behavior of a physical system over time.
Program variables (state variables) represent the current state of the physical system
Simulation program modifies state variables to model the evolution of the physical system over time.

10. Defense Simulations Types of simulation Major application areas
Constructive: simulated people operating simulated equipment
Virtual: real people operating simulated equipment,
Live: real people operating real equipment
Major application areas
Analysis
Wargaming, logistics
Training
Platform level, Command level
Test and evaluation
Hardware-in-the-loop

11. Types of Simulation Models
System model
deterministic
stochastic
static
dynamic
static
dynamic
Monte Carlo
simulation
continuous
discrete
continuous
discrete
Continuous
simulation
Discrete-event
simulation
Continuous
simulation
Discrete-event
simulation

12. Stochastic vs. Deterministic
Stochastic simulation: a simulation that contains random (probabilistic) elements, e.g.,
Examples
Inter-arrival time or service time of customers at a restaurant or store
Amount of time required to service a customer
Output is a random quantity (multiple runs required analyze output)
Deterministic simulation: a simulation containing no random elements
Simulation of a digital circuit
Simulation of a chemical reaction based on differential equations
Output is deterministic for a given set of inputs

13. Static vs. Dynamic Models
Static models
Model where time is not a significant variable
Examples
Determine the probability of a winning solitaire hand
Static + stochastic = Monte Carlo simulation
Statistical sampling to develop approximate solutions to numerical problems
Dynamic models
Model focusing on the evolution of the system under investigation over time
Main focus of this course

14. Continuous vs. Discrete
State of the system is viewed as changing at discrete points in time
An event is associated with each state transition
Events contain time stamp
Continuous
State of the system is viewed as changing continuously across time
System typically described by a set of differential equations

15. Course Overview This course is basically about going from to
An actual or envisioned system
A useful simulation model of that system
Discrete event simulation
Continuous simulation
Monte Carlo simulation
Simulation software

16. Model Development Life Cycle
Define goals, objectives of study
Develop conceptual model
Develop specification of model
Fundamentally an iterative process
Develop computational model
Verify model
Validate model

17. Determine Goals and Objectives
What does you (or the customer) hope to accomplish with the model
May be an end in itself
Predict the weather
Train personnel to develop certain skills (e.g., driving)
More often a means to an end
Optimize a manufacturing process or develop the most cost effective means to reduce traffic congestion in some part of a city
Often requires developing a business case to justify the cost
Improved efficiency will save the company $$$
Example: electronics
Even so, may be hard to justify in lean times
Goals may not be known when you start the project!
One often learns things along the way

18. Develop Conceptual Model
An abstract (i.e., not directly executable) representation of the system
What should be included in model? What can be left out?
What abstractions should be used
Level of detail
Often a variation on standard abstractions
Example: transportation
Fluid flow?
Queueing network?
Cellular automata?
What metrics will be produced by the model?
Appropriate choice depends on the purpose of the model

19. Develop Specification Model
A more detailed specification of the model including more specifics
Collect data to populate model
Traffic example: Road geometry, signal timing, expected traffic demand, driver behavior
Empirical data or probability distributions often used
Development of algorithms necessary to include in the model
Example: Path planning for vehicles

20. Develop Computational Model
Executable simulation model
Software approach
General purpose programming language
Special purpose simulation language
Simulation package
Approach often depends on need for customization and economics
Where do you make your money?
Defense vs. commercial industry
Other (non-functional) requirements
Performance
Interoperability with other models/tools/data

21. Verification Did I build the model right?
Does the computational model match the specification model?
Largely a software engineering activity (debugging)
Not to be confused with correctness (see model validation)!

22. Validation Did I build the right model?
Does the computational model match the actual (or envisioned) system?
Typically, compare against
Measurements of actual system
An analytic (mathematical) model of the system
Another simulation model
By necessity, always an incomplete activity!
Often can only validate portions of the model
If you can validate the simulation with 100% certainty, why build the simulation?

23. Summary
Modeling and simulation is an important, widely used technique with a wide range of applications
Computation power increases (Moore’s law) have made it more pervasive
In some cases, it has become essential (e.g., to be economically competitive)
Rich variety of types of models, applications, uses
As easy (actually, easier!) to get wrong or misleading answers as it is to get useful results
Appropriate methodologies required to protect against major mistakes. Even so…