© CHAMELEON RF 2005Model Order Reduction - Eindhoven – April 10-12, 2006 Course on Model Order Reduction Eindhoven, April 10-12, 2006.

Slides:

Advertisements

Similar presentations

Solution of Benchmark Problems for CO 2 Storage Min Jin, Gillian Pickup and Eric Mackay Heriot-Watt University Institute of Petroleum Engineering.

Advertisements

DIGITAL POLICY MANAGEMENT IN THE DOM PROGRAMME Richard Masters Programme Manager Digital Object Management Programme Digital Policy Management Workshop.

Introduction to the Internet

Signals and Systems – Chapter 2

10.1 Introduction Chapter 10 PID Controls

November 2006 TECO-WIS, Seoul 1 Definition of the Internet Network of networks –millions of smaller domestic, academic, business, and government networks.

Coordination of Multi-Agent Systems Mark W. Spong Donald Biggar Willett Professor Department of Electrical and Computer Engineering and The Coordinated.

Running a model's adjoint to obtain derivatives, while more efficient and accurate than other methods, such as the finite difference method, is a computationally.

CSTA K-12 Computer Science Standards (rev 2011)

CADGME, Pécs, June Piroska B. Kis, Computer aided education of numerical methods 1 College of Dunaújváros

Introduction There are various theoretical concepts and skills that bioscience students need to develop in order to become effective at solving problems.

Properties of State Variables

Robust control Saba Rezvanian Fall-Winter 88.

ECE 8443 – Pattern Recognition ECE 3163 – Signals and Systems Objectives: Review Resources: Wiki: State Variables YMZ: State Variable Technique Wiki: Controllability.

Claudia Lizet Navarro Hernández PhD Student Supervisor: Professor S.P.Banks April 2004 Monash University Australia April 2004 The University of Sheffield.

SolidWorks Simulation. Dassault Systemes 3 – D and PLM software PLM - Product Lifecycle Management Building models on Computer Engineering Analysis and.

CSE245: Computer-Aided Circuit Simulation and Verification Lecture Note 3 Model Order Reduction (1) Spring 2008 Prof. Chung-Kuan Cheng.

UCSD CSE 245 Notes – SPRING 2006 CSE245: Computer-Aided Circuit Simulation and Verification Lecture Notes 3 Model Order Reduction (1) Spring 2006 Prof.

Development of Empirical Models From Process Data

Modeling in the Time Domain - State-Space

The Design and Analysis of Algorithms

Overview of Signals & Systems Hany Ferdinando Dept. of Electrical Engineering Petra Christian University.

Introduction Social Media Mining. 2 Measures and Metrics 2 Social Media Mining Introduction Facebook How does Facebook use your data? Where do you think.

Principles of the Global Positioning System Lecture 10 Prof. Thomas Herring Room A;

VIRTUAL PROTOTYPING of ROBOTS DYNAMICS E. Tarabanov.

Stability and Passivity of the Super Node Algorithm for EM modelling of ICs Maria Ugryumova Supervisor : Wil Schilders Technical University Eindhoven,

Asymptotic Techniques

1 Chapter No 3 ICT IN Science,Maths,Modeling, Simulation.

Linear System Theory Instructor: Zhenhua Li Associate Professor Mobile ： School of Control Science and Engineering, Shandong.

Operations Research Lecturer Eng. Ahmed H. Abo absa 2 nd Semester ITGD4207 University of Palestine.

Techniques for Analysis and Calibration of Multi- Agent Simulations Manuel Fehler Franziska Klügl Frank Puppe Universität Würzburg Lehrstuhl für Künstliche.

Iterative factorization of the error system in Moment Matching and applications to error bounds Heiko Panzer, Thomas Wolf, Boris Lohmann GAMM-Workshop.

Computational Fluid Dynamics - Fall 2003 The syllabus Term project CFD references (Text books and papers) Course Tools Course Web Site:

A more reliable reduction algorithm for behavioral model extraction Dmitry Vasilyev, Jacob White Massachusetts Institute of Technology.

Digital Intuition Cluster, Smart Geometry 2013, Stylianos Dritsas, Mirco Becker, David Kosdruy, Juan Subercaseaux Welcome Notes Overview 1. Perspective.

Akram Bitar and Larry Manevitz Department of Computer Science

Motivation Thus far we have dealt primarily with the input/output characteristics of linear systems. State variable, or state space, representations describe.

A TBR-based Trajectory Piecewise-Linear Algorithm for Generating Accurate Low-order Models for Nonlinear Analog Circuits and MEMS Dmitry Vasilyev, Michał.

THE LAPLACE TRANSFORM LEARNING GOALS Definition

Numerical Analysis Intro to Scientific Computing.

Lecture 7: State-Space Modeling 1.Introduction to state-space modeling Definitions How it relates to other modeling formalisms 2.State-space examples 3.Transforming.

BART VANLUYTEN, JAN C. WILLEMS, BART DE MOOR 44 th IEEE Conference on Decision and Control December 2005 Model Reduction of Systems with Symmetries.

NAS101, Appendex A, Page 1 DOCUMENTATION This section briefly describes the MSC.Nastran documentation. A quick overview of these documents is shown in.

Understanding the difference between an engineer and a scientist There are many similarities and differences.

Introduction The STL is a complex piece of software engineering that uses some of C++'s most sophisticated features STL provides an incredible amount.

Motivation and aims The Belousov-Zhabotinsky system The Java applet References 1.Stochastic Modelling Web Module:

Unit1: Modeling & Simulation Module5: Logic Simulation Topic: Unknown Logic Value.

ENE 490 Applied Communication Systems

Dynamic Neural Network Control (DNNC): A Non-Conventional Neural Network Model Masoud Nikravesh EECS Department, CS Division BISC Program University of.

Pieter Heres, Aday Error control in Krylov subspace methods for Model Order Reduction Pieter Heres June 21, 2005 Eindhoven.

Nonlinear balanced model residualization via neural networks Juergen Hahn.

INTRODUCTION Session 1 Course: S Introduction to Finite Element Method Year: 2010.

A PID Neural Network Controller

Computational Fluid Dynamics Lecture II Numerical Methods and Criteria for CFD Dr. Ugur GUVEN Professor of Aerospace Engineering.

CHAPTER 2 - EXPLICIT TRANSIENT DYNAMIC ANALYSYS

Topic: Introduction to Computing Science and Programming + Algorithm

The Design and Analysis of Algorithms

COP 3503 FALL 2012 Shayan Javed Lecture 15

Intro to Machine Learning

Analytics and OR DP- summary.

Generalized Principal Component Analysis CVPR 2008

Model Order Reduction for Parametric Systems

Peter Benner and Lihong Feng

Petri nets refresher Prof.dr.ir. Wil van der Aalst

Nonlinear Dynamic Control Systems

Masoud Nikravesh EECS Department, CS Division BISC Program

Beijing University of Aeronautics & Astronautics

WORKSHOP AND MC MEETING TARTU, FEBRUARY 8-9, 2018

Akram Bitar and Larry Manevitz Department of Computer Science

Presentation transcript:

© CHAMELEON RF 2005Model Order Reduction - Eindhoven – April 10-12, 2006 Course on Model Order Reduction Eindhoven, April 10-12, 2006

© CHAMELEON RF 2005Model Order Reduction - Eindhoven – April 10-12, 2006 Organized by…. Centre for Analysis, Scientific Computing and Applications

© CHAMELEON RF 2005Model Order Reduction - Eindhoven – April 10-12, 2006 Model Order Reduction? Obtain a compact description of behavior by reducing the complexity of the model, using only the dominant part of the behavior

© CHAMELEON RF 2005Model Order Reduction - Eindhoven – April 10-12, 2006 Model Order Reduction? Model Order Reduction (MOR) is a branch of systems and control theory, which studies properties of dynamical systems in application for reducing their complexity, while preserving their input-output behavior. system inputoutput

© CHAMELEON RF 2005Model Order Reduction - Eindhoven – April 10-12, 2006 Goals and problems of Model Order Reduction To make a reduction process automatic (the algorithm doesn't know anything about the nature of underlying system) Sometimes we need to preserve some system properties, such as passivity, stability, etc. To ensure good approximation of the original system by the reduced system in various aspects Maybe we may vary some parameter of a system (i.e. length of transmission line). We need to be able to create parametrized reduced models. Since non-reduced models may have millions of unknowns, the algorithm must be efficient.

© CHAMELEON RF 2005Model Order Reduction - Eindhoven – April 10-12, 2006 Synonyms Model Order Reduction Reduced Order Modelling Behavioral Modelling Dimension Reduction of Large- Scale Systems ……………

© CHAMELEON RF 2005Model Order Reduction - Eindhoven – April 10-12, 2006 Books P. Benner, V. Mehrmann, D. Sorensen, Dimension Reduction of Large-Scale Systems (2005) A. Antoulas, Approximation of Large-Scale Dynamical Systems (2005) B. N. Datta, Numerical Methods for Linear Control systems (2004) G. Obinata, B. D. O. Anderson, Model Reduction for Control System Design (2004) Z. Q. Qu, Model Order Reduction Techniques with Applications in Finite Element Analysis (2005) H.A. van der Vorst, W.H.A. Schilders, Model Order Reduction: Theory and Practice (to appear)

© CHAMELEON RF 2005Model Order Reduction - Eindhoven – April 10-12, 2006 Websites 005/160/info.php3?wsid=160 (Workshop Model Order Reduction, Coupled Problems and Optimization) 005/160/info.php3?wsid=160 (Model Order Reduction website at MIT) ex.php?page= freiburg.de/simulation/benchmark/ (Oberwolfach Model Reduction Benchmark Collection) ex.php?page= freiburg.de/simulation/benchmark/

© CHAMELEON RF 2005Model Order Reduction - Eindhoven – April 10-12, 2006 More links Model order reduction page at Institut für Automatisierungstechnik, University of Bremen.Model order reduction page A very big collection of control-related aricles and theses of the Control Group at the University of Cambridge, UK.control-related aricles and theses Collection of the Model Order Reduction benchmarks for linear and nonlinear problems at the University of Freiburg, Germany.Collection of the Model Order Reduction benchmarks Another benchmark collection for model reduction from the Niconet web site.benchmark collection for model reduction Course material for "Dynamic systems and control" (6.241) course at MIT; essential for understanding dynamic systems theory.Course material for "Dynamic systems and control" ……and many others

© CHAMELEON RF 2005Model Order Reduction - Eindhoven – April 10-12, 2006 This course Will provide a thorough introduction to Model Order Reduction Starts with Basic Concepts in numerical linear algebra and systems&control Treats the linear case extensively, demonstrating different methods (Krylov based, Gramian based, POD) Discusses current research (nonlinear MOR, parametrized MOR) Several applications will be shown And hands-on experience with a variety of methods and software tools

© CHAMELEON RF 2005Model Order Reduction - Eindhoven – April 10-12, 2006 Enjoy the course! Jan ter Maten (COMSON) Siep Weiland (PROMATCH) Wil Schilders (CHAMELEON RF)