Αποτελέσματα Αναζήτησης
Lecture 2 - Modeling and Simulation • Model types: ODE, PDE, State Machines, Hybrid • Modeling approaches: – physics based (white box) – input-output models (black box) • Linear systems • Simulation • Modeling uncertainty
Subject provides an introduction to modeling and simulation. Scientists and engineers have long used models to better understand the system they study, for analysis and quantification, performance prediction and design.
Chapters 3 to 5 develop a three - part series of M & S paradigms, starting with Chapter 3 , “ Discrete - Event Simulation, ” Chapter 4 , “ Modeling Continuous Systems, ” and Chapter 5 , “ Monte Carlo Simulation. ” Chapters 6 and 7 develop two areas necessary for model development.
Objectives of the Course. Introduce the main tools for the simulation of random variables and the approximation of multidimensional integrals: Integration by Monte Carlo, inversion method, transformation method, rejection sampling, importance sampling,
Lecture 9 – Modeling, Simulation, and Systems Engineering • Development steps • Model-based control engineering • Modeling and simulation • Systems platform: hardware, systems software.
The essential skills of modeling | abstraction, analysis, simulation, and vali- dation | are central in engineering, natural sciences, social sciences, medicine, and many other elds.
Part 1 (Marjolein Dijkstra, 8 weeks, lecture notes) Programming in c. Basic concepts of simulations (Monte Carlo simulations) Writing a basic Monte Carlo program from scratch. Analyzing and plotting data. Lecture + exercises to be handed in one week later.
