1cs542g-term1-2006 Notes  Even if you’re not registered (not handing in assignment 1) send me an email to be added to a class list.

Slides:



Advertisements
Similar presentations
Ordinary Differential Equations (ODEs) Differential equations are the ubiquitous, the lingua franca of the sciences; many different fields are linked by.
Advertisements

Ordinary Differential Equations
1cs542g-term Notes. 2 Solving Nonlinear Systems  Most thoroughly explored in the context of optimization  For systems arising in implicit time.
1cs542g-term Notes  Notes for last part of Oct 11 and all of Oct 12 lecture online now  Another extra class this Friday 1-2pm.
1cs542g-term Notes  Extra class this Friday 1-2pm  Assignment 2 is out  Error in last lecture: quasi-Newton methods based on the secant condition:
CSE245: Computer-Aided Circuit Simulation and Verification Lecture Note 5 Numerical Integration Spring 2010 Prof. Chung-Kuan Cheng 1.
Atms 4320 Lab 2 Anthony R. Lupo. Lab 2 -Methodologies for evaluating the total derviatives in the fundamental equations of hydrodynamics  Recall that.
Numerical Integration CSE245 Lecture Notes. Content Introduction Linear Multistep Formulae Local Error and The Order of Integration Time Domain Solution.
ECIV 301 Programming & Graphics Numerical Methods for Engineers Lecture 31 Ordinary Differential Equations.
Initial-Value Problems
Dr. Jie Zou PHY Chapter 9 Ordinary Differential Equations: Initial-Value Problems Lecture (II) 1 1 Besides the main textbook, also see Ref.: “Applied.
Ordinary Differential Equations (ODEs) 1Daniel Baur / Numerical Methods for Chemical Engineers / Implicit ODE Solvers Daniel Baur ETH Zurich, Institut.
Adaptive Runge-Kutta addresses the problem of functions that change rapidly at a point.
Chapter 16 Integration of Ordinary Differential Equations.
CISE301_Topic8L31 SE301: Numerical Methods Topic 8 Ordinary Differential Equations (ODEs) Lecture KFUPM (Term 101) Section 04 Read , 26-2,
Ordinary Differential Equations (ODEs)
Numerical Solution of Ordinary Differential Equation
MATH 685/ CSI 700/ OR 682 Lecture Notes Lecture 10. Ordinary differential equations. Initial value problems.
Ordinary Differential Equations (ODEs) 1Daniel Baur / Numerical Methods for Chemical Engineers / Explicit ODE Solvers Daniel Baur ETH Zurich, Institut.
Numerical Solutions to ODEs Nancy Griffeth January 14, 2014 Funding for this workshop was provided by the program “Computational Modeling and Analysis.
Boyce/DiPrima 9th ed, Ch 8.4: Multistep Methods Elementary Differential Equations and Boundary Value Problems, 9th edition, by William E. Boyce and Richard.
Ch 8.3: The Runge-Kutta Method
Erin Catto Blizzard Entertainment Numerical Integration.
EE3561_Unit 8Al-Dhaifallah14351 EE 3561 : Computational Methods Unit 8 Solution of Ordinary Differential Equations Lesson 3: Midpoint and Heun’s Predictor.
Lecture 3.
1 EEE 431 Computational Methods in Electrodynamics Lecture 4 By Dr. Rasime Uyguroglu
Computer Animation Algorithms and Techniques
Modelling & Simulation of Chemical Engineering Systems Department of Chemical Engineering King Saud University 501 هعم : تمثيل الأنظمة الهندسية على الحاسب.
Integration of 3-body encounter. Figure taken from
Copyright © 2006 The McGraw-Hill Companies, Inc. Permission required for reproduction or display. ~ Ordinary Differential Equations ~ Stiffness and Multistep.
Copyright © 2006 The McGraw-Hill Companies, Inc. Permission required for reproduction or display. by Lale Yurttas, Texas A&M University Chapter 261 Stiffness.
+ Numerical Integration Techniques A Brief Introduction By Kai Zhao January, 2011.
Finite Difference Methods Definitions. Finite Difference Methods Approximate derivatives ** difference between exact derivative and its approximation.
Ordinary Differential Equations (ODEs) 1Michael Sokolov / Numerical Methods for Chemical Engineers / Explicit ODE Solvers Michael Sokolov ETH Zurich, Institut.
Numerical Analysis – Differential Equation
Introduction to the Runge-Kutta algorithm for approximating derivatives PHYS 361 Spring, 2011.
Ch 8.2: Improvements on the Euler Method Consider the initial value problem y' = f (t, y), y(t 0 ) = y 0, with solution  (t). For many problems, Euler’s.
Dr. Mujahed AlDhaifallah ( Term 342)
Today’s class Ordinary Differential Equations Runge-Kutta Methods
Lecture 40 Numerical Analysis. Chapter 7 Ordinary Differential Equations.
ECE 576 – Power System Dynamics and Stability Prof. Tom Overbye Dept. of Electrical and Computer Engineering University of Illinois at Urbana-Champaign.
Ordinary Differential Equations
Sec 21: Generalizations of the Euler Method Consider a differential equation n = 10 estimate x = 0.5 n = 10 estimate x =50 Initial Value Problem Euler.
1/14  5.2 Euler’s Method Compute the approximations of y(t) at a set of ( usually equally-spaced ) mesh points a = t 0 < t 1
Lecture 39 Numerical Analysis. Chapter 7 Ordinary Differential Equations.
1 Spring 2003 Prof. Tim Warburton MA557/MA578/CS557 Lecture 32.
The Finite-Difference Method Taylor Series Expansion Suppose we have a continuous function f(x), its value in the vicinity of can be approximately expressed.
Lecture 11 Alessandra Nardi
Algebra 1 Section 6.5 Graph linear inequalities in two variables.
ECE 576 – Power System Dynamics and Stability
CSE245: Computer-Aided Circuit Simulation and Verification
SE301: Numerical Methods Topic 8 Ordinary Differential Equations (ODEs) Lecture KFUPM (Term 101) Section 04 Read , 26-2, 27-1 CISE301_Topic8L4&5.
Class Notes 19: Numerical Methods (2/2)
CSE 245: Computer Aided Circuit Simulation and Verification
Finite Volume Method for Unsteady Flows
Chapter 26.
Introduction to Scientific Computing II
Homework 3 Q1) Build a finite-difference solver for: with initial condition: Q1a) use Heun’s RK3 time integrator Q1b) use the 4th order central difference.
CSE245: Computer-Aided Circuit Simulation and Verification
ECE 576 POWER SYSTEM DYNAMICS AND STABILITY
5.3 Higher-Order Taylor Methods
ECE 576 POWER SYSTEM DYNAMICS AND STABILITY
Overview Class #2 (Jan 16) Brief introduction to time-stepping ODEs
SE301: Numerical Methods Topic 8 Ordinary Differential Equations (ODEs) Lecture KFUPM Read , 26-2, 27-1 CISE301_Topic8L3 KFUPM.
Implicit and Explicit Runge-Kutta methods
6th Lecture : Numerical Methods
Differential equations
EE 616 Computer Aided Analysis of Electronic Networks Lecture 12
Presentation transcript:

1cs542g-term Notes  Even if you’re not registered (not handing in assignment 1) send me an to be added to a class list

2cs542g-term Time Integration  Recall Taylor series  Linearization in higher dimensions  Reduction to first order systems  Well-posed problems: problem stability  Linear analysis

3cs542g-term Forward Euler  Accuracy: truncation error vs. global error  Algorithm stability  Test Equation  Stability region

4cs542g-term More accurate explicit methods  Leapfrog  Multistep/multivalue methods  Runge-Kutta methods Heun’s method (RK2) Standard RK4

5cs542g-term Error control  Adaptive time stepping  Integrator pairs

6cs542g-term Stiffness  A problem is stiff when an explicit integrator needs to take small steps for stability, not accuracy Solution space includes very rapidly vanishing transients  Note: not stiff just because you want to take large time steps If the real solution has variations on a small timescale, there’s no avoiding small time steps  Usually handled with implicit integrators  Backwards Euler