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MPhys Physics with Astrophysics

Year of entry: 2021

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Course unit details:
Computational Physics

Unit code	PHYS20762
Credit rating	10
Unit level	Level 2
Teaching period(s)	Semester 2
Offered by	Department of Physics & Astronomy
Available as a free choice unit?	No

Overview

Computational Physics

Pre/co-requisites

Unit title	Unit code	Requirement type	Description
Introduction to Programming for Physicists	PHYS20161	Pre-Requisite	Compulsory

Aims

To give an introduction to the techniques of computational physics and dynamic high-level scripting programming languages.

Learning outcomes

This course unit detail provides the framework for delivery in 20/21 and may be subject to change due to any additional Covid-19 impact. Please see Blackboard / course unit related emails for any further updates’

On completion successful students will be able to:

1. Write programs using dynamic high-level scripting programming languages and carry out data analysis in them.

2. Use classical numerical methods (Euler and higher order) to find solutions of ordinary differential equations.

3. Use Monte Carlo techniques and associated statistical methods.

4. Use numerical solutions to analyse the behaviour of a physical system (such as a driven oscillator).

Syllabus

1. Use of high-level scripting language for data analysis.

a) Definitions of variables and arrays; scalar and array operations; built in and user-defined functions;

b) Working with data sets: file input / output. Data visualization and plotting;

c) Revision of error analysis: X2 analysis, errors on fitting coefficients, propagation of errors;

d) Comparison of different high-level languages.

Project 1

2. Numerical methods and the solution of ordinary differential equations

a) Introduction to numerical computing; errors in numerical methods;

b) Numerical methods for solving ordinary differential equations; Euler’s method; higher order methods; symplectic methods;

c) Implementation of numerical methods;

d) The linear driven damped oscillator; phase space; conserved quantities; sources of simulation error;

e) Introduction to nonlinear systems.

Project 2

3. The Monte Carlo method and its applications.

a) Introduction to Monte Carlo methods; Monte Carlo integration; classical problems;

b) Pseudorandom sampling; methods of generating samples with given probability density;

c) Applications of Monte Carlo methods;

d) Statistical errors.

Project 3

Feedback methods

Feedback will be given orally by demonstrators during lab sessions, and written and oral feedback of the written project work will be given.

Study hours

Scheduled activity hours
Lectures	12
Practical classes & workshops	36

Independent study hours
Independent study	52

Teaching staff

Staff member	Role
Draga Pihler-Puzovic	Unit coordinator

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