Computational Physics Introduction 3/30/11. Goals  Calculate solutions to physics problems  All physics problems can be formulated mathematically. 

Slides:



Advertisements
Similar presentations
Chapter 6 Differential Equations
Advertisements

Beginning Programming for Engineers
Linear Programming. Introduction: Linear Programming deals with the optimization (max. or min.) of a function of variables, known as ‘objective function’,
Chapter 1 Introduction The solutions of engineering problems can be obtained using analytical methods or numerical methods. Analytical differentiation.
Transfer Functions Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: The following terminology.
Transfer Functions Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: The following terminology.
Feedback Control Systems (FCS)
Numerical Solution of Ordinary Differential Equation
Lecture 24 Introduction to state variable modeling Overall idea Example Simulating system response using MATLAB Related educational modules: –Section 2.6.1,
Chapter 7 Matrix Mathematics Matrix Operations Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
Section 8.3 – Systems of Linear Equations - Determinants Using Determinants to Solve Systems of Equations A determinant is a value that is obtained from.
Numerical Methods Due to the increasing complexities encountered in the development of modern technology, analytical solutions usually are not available.
WordDefinitionExample Equation A mathematical statement that says two expressions are equal Inequality A mathematical statement that compares two expressions.
Chapter 10 Review: Matrix Algebra
Accurate Implementation of the Schwarz-Christoffel Tranformation
INTRODUCTION FOR PERL MONGERS MATLAB. Outline 1. Matlab, what is it good for 2. Matlab’s IDE & functions 3. A few words about Maple 4. What needs to be.
National 5 Mathematics Expressions and Formulae ApplicationsRelationships N5.
An equation is a mathematical statement that two expressions are equivalent. The solution set of an equation is the value or values of the variable that.
MMJ 1113 Computational Methods for Engineers Mohsin Mohd Sies Fakulti Kejuruteraan Mekanikal, Universiti Teknologi Malaysia.
MATH 685/CSI 700 Lecture Notes Lecture 1. Intro to Scientific Computing.
Analytical vs. Numerical Minimization Each experimental data point, l, has an error, ε l, associated with it ‣ Difference between the experimentally measured.
ME451 Kinematics and Dynamics of Machine Systems Numerical Solution of DAE IVP Newmark Method November 1, 2013 Radu Serban University of Wisconsin-Madison.
Application of Differential Applied Optimization Problems.
CSE 541 Rick Parent ELEMENTARY NUMERICAL METHODS Winter 2012.
O AK R IDGE N ATIONAL L ABORATORY U.S. D EPARTMENT OF E NERGY 1 Parallel Solution of the 3-D Laplace Equation Using a Symmetric-Galerkin Boundary Integral.
Numerical analysis in 10mins Zhiyun (Kevin) Kuang.
National 4 Mathematics Expressions and Formulae NumeracyRelationships N4.
MA3264 Mathematical Modelling Lecture 3 Model Fitting.
Computational fluid dynamics Authors: A. Ghavrish, Ass. Prof. of NMU, M. Packer, President of LDI inc.
Higher Mathematics Expressions and Functions Applications Relationships and Calculus H.
Introduction Examples of differential equations and related problems Analytical versus numerical solutions Ideas of numerical representations of solutions.
The Islamic University of Gaza Faculty of Engineering Civil Engineering Department Numerical Analysis ECIV 3306 Introduction Course Outline.
Numerical Methods.
An Introduction to Heat Transfer Morteza Heydari.
Numerical Analysis Intro to Scientific Computing.
What is Calculus ? Three Basic Concepts Lesson 2.1.
1 ECE 1304 Introduction to Electrical and Computer Engineering Section 1.7 Linear Algebra with MATLAB.
Arclength & Approximating Integrals. Solution: We plot the graph for convenience. We obtain the formula:
Numerical Analysis. Numerical Analysis or Scientific Computing Concerned with design and analysis of algorithms for solving mathematical problems that.
Advanced Engineering Mathematics, 7 th Edition Peter V. O’Neil © 2012 Cengage Learning Engineering. All Rights Reserved. CHAPTER 4 Series Solutions.
Hands-on Introduction to Mathematica. Primarily a computer program to do symbolic algebra and calculus Does Plotting, Numerical and Web stuff too. Very.
ERT 210/4 Process Control & Dynamics DYNAMIC BEHAVIOR OF PROCESSES :
WEEK 6 Day 1. Progress report Thursday the 11 th.
General Analysis Procedure and Calculator Policy Calculator Policy.
S3-1 ADM703, Section 3, August 2005 Copyright  2005 MSC.Software Corporation SECTION 3 SUSPENSION SYSTEM.
Algebra Review. Systems of Equations Review: Substitution Linear Combination 2 Methods to Solve:
Solving Multi-Step Equations INTEGRATED MATHEMATICS.
Modelling & Simulation of Semiconductor Devices Lecture 1 & 2 Introduction to Modelling & Simulation.
Scientific Computing: Does Anyone Care? Alan Kaylor Cline Department of Computer Sciences The University of Texas at Austin October 30, 2008 ACM 101 Lecture.
S5.40. Module Structure 30% practical tests / 70% written exam 3h lectures / week (except reading week) 3 x 2h of computer labs (solving problems practicing.
Solving Engineering Problems
Higher Mathematics.
244-1: INTRODUCTION TO PROGRAMMING
Operations Research Chapter one.
Transfer Functions Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: The following terminology.
Introduction Mathcad is a product of mathSoft inc. The Mathcad can help us to calculate, graph, and communicate technical ideas. It lets us work with.
MTH1170 Differential Equations
An Introduction to Maple
Numerical Approximation
MATH 2140 Numerical Methods
MATH My research interests lie primarily in the area of numerical analysis and scientific computing, …
Solving Engineering Problems
Analytical Tools in ME Course Objectives
Simultaneous Equations
Discrete Least Squares Approximation
Coordinate Transformation in 3D Final Project Presentation
Graphical Solutions of Trigonometric Equations
Numerical Computation and Optimization
Reading Between the Lines!
Presentation transcript:

Computational Physics Introduction 3/30/11

Goals  Calculate solutions to physics problems  All physics problems can be formulated mathematically.  Many strategies for solving equations.  Display solutions in a way that helps us interpret the physics

Division of Labor  Most of the work in solving a problem is still done in the physicist’s head  Derive the equations that represent the system of interest  Understand all the approximations and limitations (conditions for validity)  Determine how to instruct the computer to solve the equations  The computer does what it is told to do.  The physicist has to interpret the computer’s output  Computational solutions are theoretical predictions (based on the equations). They must eventually be compared to measurements.

equations  Many types of equations:  Algebraic  Trigonometric, logorithmic  Differential, integral  Linear, nonlinear  You may have a set of equations that must be solved simultaneously  Matrix manipulations, linear algebra  Your equations may depend on initial conditions or boundary conditions

Solving equations  Some special equations have an analytical, or “closed-form,” solution, which is a certain known function or combination of functions.  Most equations (or sets of equations) must be solved numerically, using a computer. The resulting solution is approximate, and consists only of a set of numbers

Visualizing results  In most cases, you will want to make a plot of the solution, in order to visualize how certain quantities depend on others.  This is something that a computer is especially good at.

Strategies  Programs such as Mathematica and MATLAB can help you work with equations analytically  Symbolic manipulation  Might help you obtain analytical solution  Most physics problems (that are not highly simplified) involve equations that must be solved numerically.  Smooth funtions must be discretized  Derivatives become differences  Integrals become sums  Errors of approximation must be carefully tracked

Numerical solutions  Can be done with Mathematica or MATLAB  Actual solving strategies are built in  Need to understand limitations  Can use a scientific programming language such as FORTAN or C  Many basic strategies have already been written  Use library of routines - customize to your problem  Solution must be plotted to be useful  Special graphics programs are available  Mathematica & MATLAB do this well, too