Tools & Resources for Teaching

Undergraduate Computational

Physics

Amde A, Dept. of Physics, University of Gondar, P.O. Box 196,

Ethiopia, mdselassie@gmail.com

“Computers are incredibly fast, accurate, and stupid; humans are incredibly

slow, inaccurate, and brilliant; together they are powerful beyond imagination”.

- Albert Einstein

“Computational scientists solve tomorrow’s problems with yesterday’s

computers; computer scientists seem to do it the other way round”.

- (Unknown)

What is Computational Physics?

A multidisciplinary subject that uses computing approach to

gain understanding in complex systems, like QCD,

electronic structure, molecular dynamics, nuclear fusion,

climate dynamics, relativistic astrophysics.

Provides the capability to study and gain novel insights into

physical systems, especially for those where no analytical

solutions can be found or an experimental study is too

complicated or expensive.

An essential and integral part of contemporary sciences,

and has become as important as theory and experiment.

Computational Physics is multidisciplinary. It combines disciplines like Computer Science and

Mathematics with Physics (Landau)

Computation is as important as theory & experiment Use of computation and simulation has now become an

essential and integral part of contemporary basic and

applied sciences (Landau)

Some Uses of Computers in Physics (Gould)

I. Numerical analysis

II. Symbolic manipulation

III. Visualization

IV. Simulation

V. Data acquisition & analysis

VI. Technology Enabled Learning

I. Numerical Analysis - Creates, analyzes, & implements algorithms

for solving numerically the problems of continuous math.

Used to compute multidimensional integral & differential

equations, manipulate large matrices, or solve linear & nonlinear

differential equations

Some software: MATLAB, LabVIEW, GNU Octave & SciLab

II. Symbolic Manipulation

Used to manipulate differentiation, integration, matrix

inversion, power series expansion, …

Some software: Mathematica, Maple, Maxima & SAGE

III. Visual Representation (Gould)

◦ The Eye + Visual Processing Capacity of the Brain

◦ Patterns & trends that might not be evident from tables of data

can be determined. Example: Run Sfile\osp_qm_twostate.jar

◦ Changes be observed & can lead to insight into the important

mechanisms underlying a system’s behavior

IV. Technology Enabled Learning

developed by integrating education research results with

technology tools

transform learning from teacher-centered to student-centered

requires new physical and cognitive architecture

Some Examples

1. Lecture Based Model - Just in Time Teaching, Peer Instruction,

Interactive Lecture Demonstrations, …

2. TEAL (Technology Enabled Active Learning),

http://icampus.mit.edu/projects/teal/

3. SCALEUP (Student Centered Active Learning

Environment with Upside down Pedagogy)

http://scaleup.ncsu.edu/

4. Laboratory:

a. Remote Controlled Lab, i-Lab

real experimental facilities/setups that can be controlled

remotely over the internet.

Examples:

◦ robotic telescopes, rcl (http://rcl.physik.uni-kl.de/),

◦ i-labs (http://icampus.mit.edu/iLabs/default.aspx)

b. Virtual Lab

interactive pc-based activity where students conduct or create

simulated experiments. Virtual instruments & apparatus that

simulate the functions & characteristics of real ones are used.

Examples:

◦ Run: Sfile\ub_optics.jar

◦ PhET Interactive Simulation: https://phet.colorado.edu/

Example:

Run: Sfile\nve_damping.ms9

5. Data Acquisition & Analysis

◦ Computer based measurement that requires interfacing the

computer with various instrumentations & sensors

◦ Involves real-time control & interactive data analysis

◦ You need: computer hardware, software, sensors, interfaces

Example:

◦ Software: LabVIEW

o Sensors & Interfaces/DAQ Boards

Example: Determination of Coefficient of Linear Expansion

The Importance of Computer Simulation

Simulation – implementing a model on a computer (to understand

its behavior & predictions)

Frequently uses computational tools of numerical analysis &

visualization.

Why is computation becoming so important in physics?

1. Explore nonlinear phenomena. Many natural phenomena

are nonlinear & most are difficult to solve by analytical

methods. Example: Download & Run Sfile\ejs_sip_ch06.jar

2. Investigate systems with many variables or with many

degrees of freedom

3. Investigate systems whose experimental studies are too

complicated & expensive.

Language & Content Selection Dilemma

Though undergraduate computational physics (UCP) is now

becoming an integral part of the physics curriculum, there is

no still standardization in the contents & programming

language used to teach the course.

Content Options?

◦ Linear Systems,

◦ Non-linear, Chaotic & Multi-variable Systems, or

◦ Both (Linear & Non-linear)

Language Options?

◦ Symbolic or Numeric Computing Environment (MATLAB,

Octave, Mathematica, Maple …)

◦ Non-Specific Pseudo-code (Giordano), or

◦ General Purpose (FORTRAN, C/C++, Java, Python …)

… Language & Content

The decision of what contents & languages to use to teach

UCP is not an easy one, but depends mostly on:

◦ Objectives of the UCP Course and time allotted for it,

◦ Computing skills & experience of both Instructors and

Students,

◦ Availability & cost of Computational Resources and

Facilities,

◦ Numerical performance, ease of use, good graphical

interfaces, & Web-based & database driven applications

of Language/Computing Environment.

… Language & Content

At University of Gondar we chose following contents &

language to teach UCP & have been using it since 2005:

◦ Both (Linear & Non-linear Systems) for Content, &

◦ Java for Language

◦ Currently, we are considering using Java with Python.

Why Linear & Nonlinear Contents?

◦ The course focuses on studying & developing simulation

for nonlinear & many variable systems, but

◦ Linear systems are used for introducing the basics of the

Language & Numerical Methods

… Language & Content

Why Java?

◦ Platform independent; (API, neutral byte code, JVM)

◦ Flexible standard graphics libraries & good performance

◦ Web-based & server-based applets

◦ Free and relatively simple to learn

◦ Supported by OSP (OpenSourcePhysics), extensive library of

open-source Java routines for computational work

(www.compadre.org/OSP/ ).

Disadvantage:

◦ Despite the high level of abstraction and platform

independence, the speed & performance of Java is not

comparable with C++ or Fortran.

◦ It is not supported by numerical libraries.

Installation

Download the JDK Installer (from www.java.com or

www.oracle.com/technetwork/java/javase/downloads/index.html)

JDK on Windows:

Run the JDK Installer & follow the instructions. (JDK should

be installed in the directory C:\ )

Add the jdk/bin directory into the execution path

JDK on Linux (for root users):

Uncompress the binary file (for 64-bit & update 60):

tar zxvf jdk-8u60-linux-x64.tar.gz

Move the JDK directory to /usr/lib

sudo mkdir -p /usr/lib/jvm

sudo mv /jdk1.8.60 /usr/lib/jvm/

… Installation

JDK on Linux:

Now enable Java

sudo update-alternatives --install "/usr/bin/java" "java"

"/usr/lib/jvm/jdk1.8.0_60/bin/java" 1

sudo update-alternatives --install "/usr/bin/javac" "javac"

"/usr/lib/jvm/jdk1.8.0_60/bin/javac" 1

sudo update-alternatives --install "/usr/bin/javaws" "javaws"

"/usr/lib/jvm/jdk1.8.0_60/bin/javaws" 1

Change the file ownership & permissions of the

executables; use the chmod a+x command.

Configure Project/Development Environment

(Based on OpenSourcePhysics & An Introduction to Computer Simulation

Methods, 3ed, by Gould & Tobochinik)

Windows:

Create a Project Directory (e.g. “C:\cp_projects”)

Download the OpenSourcePhysics Eclipse Workspace (from

www.compadre.org/OSP/ ) and unpack the source code for the

OSP core library (src.zip) in to the Project Dir.

Create a Dir. named classes under the Project Dir.:

C:\cp_projects\classes

Create a Dir. named org under classes & a Dir. named

opensourcephysics under the org Dir.:

C:\cp_projects\classes\org\opensourcephysics

Copy the resource Dir. from …src\org\opensourcephysics to

…classes\org\opensourcephysics

…Configure Project

Linux:

Create a Project Dir. under the user’s Home Dir., unpack

the core library src.zip, & make directory named classes:

mkdir /cp_projects

unzip /cp_projects/src.zip

mkdir /cp_projects/classes

Create a directory named org under classes & a directory

named opensourcephysics under the org Dir.:

mkdir /cp_projects/classes/org/opensourcephysics

Copy the resource Dir. from …src\org\opensourcephysics to

…classes\org\opensourcephysics

Edit

Windows:

the source code can be edited using Notepad (++) &

saved as “FileName”.java under Directory:

C:\cp_project\src\”package”

Linux:

the source code can be edited using text editor gedit &

saved as “FileName”.java under Directory:

home/”user”/cp_project/src/”package”

… Edit (Example using Notepad ++) (Gould)

Compile

Windows:

Open Command Prompt (C:\_)

Change Directory to Project Directory (C:\cp_project)

Compile:

javac –d classes/ -sourcepath src/ src/”package”/”FileName”.java

Linux:

Open the Terminal

Change Directory to Project Directory

cd cp_project

Compile:

javac -d classes/ -sourcepath src/ src/”package”/”FileName”.java

… Compile (Example)

Run

Windows:

Open Command Prompt (C:\_)

Change Directory to Project Directory (C:\cp_project)

Run:

java -classpath classes/ “package name”/”FileName”

Linux:

Open the Terminal

Change Directory to Project Directory

cd cp_project

Run:

java -classpath classes/ “package name”/”FileName”

… Run (Example & Results) (Gould)

References & Resources

1. Landau, A First Course in Scientific Computing. Princeton Univ. Press, 2005.

2. Gould & Tobochnik, An Introduction to Computer Simulation Methods, 3ed.

Addison-Wesley 2006.

3. OpenSourcePhysics, www.compadre.org/OSP/

4. Giordano & Nakanishi, Computational Physics, 2ed. Prentice Hall 2005.

5. Cook, Computation and Problem Solving in Undergraduate Physics.

http://www.lawrence.edu/dept/physics/ccli

6. Yevick, A First Course in Computational Physics & Object-Oriented

Programming with C++. Cambridge Univ. Press, 2005.

7. Hjorth-Jensen, Computational Physics Lecture Notes, Univ. of Oslo, 2012

8. Thijssen, Computational Physics, Cambridge Univ. Press, 1999

9. Gershenfeld, The Nature of Mathematical Modeling. Cambridge Univ. P, 1999

10. Landau, Computational Physics: A Better Model for Physics Education?

Computing in Science & Engineering, 2006