Modeling and Simulation: Fundamentals and Implementation Introduction

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

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. [www.learnthat.com] 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] !!!

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

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

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

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)

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

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.

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

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

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

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

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

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

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

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

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

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

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

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)!

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?

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…