1.2 The Modeling Process.

Slides:



Advertisements
Similar presentations
© Loughborough University, 2004 Types of Systems There are a number of ways in which we may define types of systems.
Advertisements

Modeling of Complex Social Systems MATH 800 Fall 2011.
SAMSI Kickoff 11/9/06Slide 1 Simulators, emulators, predictors – Validity, quality, adequacy Tony O’Hagan.
Modeling and Simulation By Lecturer: Nada Ahmed. Introduction to simulation and Modeling.
Introduction into Simulation Basic Simulation Modeling.
Dealing with Complexity Robert Love, Venkat Jayaraman July 24, 2008 SSTP Seminar – Lecture 10.
Modeling and simulation of systems Slovak University of Technology Faculty of Material Science and Technology in Trnava.
In this handout Stochastic Dynamic Programming
GoldSim 2006 User Conference Slide 1 Vancouver, B.C. The Submodel Element.
Decision Making: An Introduction 1. 2 Decision Making Decision Making is a process of choosing among two or more alternative courses of action for the.
FIN 685: Risk Management Topic 5: Simulation Larry Schrenk, Instructor.
Classification of Simulation Models
Negative vs. positive feedback What is the difference? Consider what happens when there is a perturbation Positive feedback drives op amp into saturation:
XYZ 6/18/2015 MIT Brain and Cognitive Sciences Convergence Analysis of Reinforcement Learning Agents Srinivas Turaga th March, 2004.
Simulation concepts and architectures. Simulation Basics System: a collecting of entities that act and interact together toward the accomplishment of.
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Continuous random variables Uniform and Normal distribution (Sec. 3.1, )
Simulation Models as a Research Method Professor Alexander Settles.
Descriptive Modelling: Simulation “Simulation is the process of designing a model of a real system and conducting experiments with this model for the purpose.
Feedback Control Systems (FCS)
Mathematical Modeling What is it? (and how do you spell it?)
Operations Research I Lecture 1-3 Chapter 1
A PowerPoint presentation brought to you by Christian Malone and Alissa Ousley.
Computer Simulation A Laboratory to Evaluate “What-if” Questions.
Policy Generation for Continuous-time Stochastic Domains with Concurrency Håkan L. S. YounesReid G. Simmons Carnegie Mellon University.
Modeling and Simulation
Discrete Event Systems Simulation
Business Modeling Lecturer: Ing. Martina Hanová, PhD.
Modeling & Simulation: An Introduction Some slides in this presentation have been copyrighted to Dr. Amr Elmougy.
Computer Science 101 Modeling and Simulation. Scientific Method Observe behavior of a system and formulate an hypothesis to explain it Design and carry.
Benjamin Gamble. What is Time?  Can mean many different things to a computer Dynamic Equation Variable System State 2.
GoldSim 2006 User Conference Slide 1 Vancouver, B.C. Event-Driven Models.
1 Definition of System Simulation: The practice of building models to represent existing real-world systems, or hypothetical future systems, and of experimenting.
Validation Dr Andy Evans. Preparing to model Verification Calibration/Optimisation Validation Sensitivity testing and dealing with error.
Math 449 Dynamical systems in Biology and Medicine. D. Gurarie Overview.
ECE 466/658: Performance Evaluation and Simulation Introduction Instructor: Christos Panayiotou.
Simulation Techniques Overview Simulation environments emulation/ exec- driven event- driven sim trace- driven sim stochastic sim Workload parameters System.
8/22/01J. Hagstrom, U. of Illinois1 Simulation Jane Hagstrom.
MA354 An Introduction to Math Models (more or less corresponding to 1.0 in your book)
1 1 Slide Simulation Professor Ahmadi. 2 2 Slide Simulation Chapter Outline n Computer Simulation n Simulation Modeling n Random Variables and Pseudo-Random.
Monte-Carlo based Expertise A powerful Tool for System Evaluation & Optimization  Introduction  Features  System Performance.
MONTE CARLO ANALYSIS When a system contains elements that exhibit chance in their behavior, the Monte Carlo method of simulation may be applied.
Modeling and Simulation Dr. X. Topics What is Continuous Simulation Why is it useful? Continuous simulation design.
MA354 Math Modeling Introduction. Outline A. Three Course Objectives 1. Model literacy: understanding a typical model description 2. Model Analysis 3.
Operations Research Models and Methods Advanced Operations Research Reference: Operations Research Models and Methods, Operations Research Models and Methods,
Csci 418/618 Simulation Models Dr. Ken Nygard, IACC 262B
Modeling & Simulation of Dynamic Systems (MSDS)
System Analysis System – set of interdependent elements that interact in order to accomplish a one or more final outcomes. Constrained and affected by:
DEPENDABILITY ANALYSIS (towards Networked Information Systems) Ester Ciancamerla, Michele Minichino ENEA {ciancamerlae, In.
Introduction The objective of simulation – Analysis the system (Model) Analytically the model – a description of some system intended to predict the behavior.
Module 9.2 Simulations. Computer simulation Having computer program imitate reality, in order to study situations and make decisions Applications?
Chapter 1 What is Simulation?. Fall 2001 IMSE643 Industrial Simulation What’s Simulation? Simulation – A broad collection of methods and applications.
Modelling & Simulation of Semiconductor Devices Lecture 1 & 2 Introduction to Modelling & Simulation.
Traffic Simulation L2 – Introduction to simulation Ing. Ondřej Přibyl, Ph.D.
Simulation Modeling.
Modeling and Simulation (An Introduction)
System Development Process
Analytics and OR DP- summary.
Simulation Examples STOCHASTIC PROCESSES.
DSS & Warehousing Systems
Prepared by Lee Revere and John Large
What is the future of applied mathematics? Chris Budd.
أنماط الإدارة المدرسية وتفويض السلطة الدكتور أشرف الصايغ
Overview of Models & Modeling Concepts
Goals of Psychology!.
Modeling and Simulation: Fundamentals and Implementation
GCSE: Quadratic Inequalities
Test Process “V” Diagram
MECH 3550 : Simulation & Visualization
SIMULATION IN THE FINANCE INDUSTRY BY HARESH JANI
Topic: Plot, Setting, and Character
Presentation transcript:

1.2 The Modeling Process

What is Modeling? Modeling is the application of methods to analyze complex, real-world problems in order to make predictions about what might happen with various actions.

Stochastic* Behavior A system exhibits probabilistic or stochastic behavior if an element of chance exists. A probabilistic or stochastic model exhibits random effects, whereas a deterministic model does not. *Στόχος = “aim” or “guess”

Static vs. Dynamic Models A static model does not consider time, whereas a dynamic model changes with time. Πάντα ῥεῖ Heraclitus (ca. 535–475 BC)

Continuous vs. Discrete In a continuous model, time changes continuously, while in a discrete model, time changes in incremental steps. Zeno of Elea (ca. 490 BC? – ca. 430 BC?)

Verification vs. Validation The process of verification determines if the solution works correctly, while the process of validation establishes if the system satisfies the problem’s requirements.