simulation and-modeling
TRANSCRIPT
Modeling and Simulation
ModelA model (usually miniature) is a
representation of the construction and working of some system of interest
It is similar to but simpler than the system it represents
A good model is a tradeoff between realism and simplicity.
Modeling and Simulation (Cont…)
It is 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 realism of their intended use.
Modeling and Simulation (Cont…)Simulation
simulation is a tool to evaluate the performance of a system , existing or proposed, under different configurations of interest and over long periods of real time.
Simulation of a system is the operation of a model of the system. The operation of the model can be studied, and hence, properties concerning the behavior of the actual system or its subsystems can be inferred.
Why Simulate?It may be too difficult, dangerous, 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 mistakesOptimize designAnalyze operational capabilities of systemsCreate virtual environments for training, entertainment
Applications: System Analysis“Classical” application of simulation
Telecommunication networksTransportation systemsElectronic systems (e.g., microelectronics,
computer systems)Battlefield simulationsManufacturing systemsLogistics
Virtual EnvironmentsUses: training (e.g., military, medicine,
emergency planning), entertainmentSimulations are often used in virtual
environments to create dynamic computer generated entitiesAdversaries and helpers in video gamesDefense: Computer generated forces (CGF)
Automated forces Semi‐automated forces
Virtual Environments (Cont…)
Physical phenomena Trajectory of projectiles Buildings “blowing up” Environmental effects on environment (e.g., rain
washing out terrain)
Simulation FundamentalsA 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 systemSimulation program modifies state variables to
model the evolution of the physical system over time.
Defense SimulationsTypes of simulation
Constructive: simulated people operating simulated equipment
Virtual: real people operating simulated equipment,
Live: real people operating real equipmentMajor application areas
AnalysisWar gaming, logistics
Stochastic vs. DeterministicStochastic 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 serve a customer
Output is a random quantity (multiple runs required analyze output)
Stochastic vs. DeterministicDeterministic simulation: a simulation
containing no random elementsExamples
Simulation of a digital circuit Simulation of a chemical reaction based on
differential equations
Output is deterministic for a given set of inputs
Static vs. Dynamic ModelsStatic models
Model where time is not a significant variableExamples
Determine the probability of a winning solitaire hand
Static + stochastic = Monte Carlo simulation Statistical sampling to develop approximate
solutions to numerical problems
Dynamic modelsModel focusing on the evolution of the system
under investigation over time
Continuous vs. DiscreteDiscrete
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
ContinuousState of the system is viewed as changing
continuously across timeSystem typically described by a set of differential
equations
Determine Goals and ObjectivesWhat do you (or the customers) hope to
accomplish with the modelMay 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
Determine Goals and ObjectivesOften requires developing a business case to
justify the costImproved efficiency will save the company
moneyEven 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
Develop Conceptual ModelAn abstract (i.e., not directly executable)
representation of the systemWhat should be included in model? What can
be left out?What abstractions should be used
Level of detailOften a variation on standard abstractions
Develop Conceptual Model
Example: transportation Fluid flow? Queuing network? Cellular automata?
What metrics will be produced by the model?Appropriate choice depends on the purpose of
the model
Develop Computational ModelExecutable simulation modelSoftware approach
General purpose programming languageSpecial purpose simulation languageSimulation package
Approach often depends on need for customization and economicsWhere do you make your money?Defense vs. commercial industry
Other (non‐functional) requirementsPerformance Interoperability with other models/tools/data
VerificationDid 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)!
ValidationDid I build the right model?Does the computational model match the actual (or
envisioned) system?Typically, compare against
Measurements of actual systemAn analytic (mathematical) model of the systemAnother 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?
SummaryModeling and simulation is an important, widely
used technique with a wide range of applicationsComputation power increases (Moore’s law) have
made it more pervasiveIn 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…