PH3080 Computational Physics

2024 to 2025 Semester 1

Curricular information may be subject to change

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 9

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Module Staff

TBC

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

This module is designed to develop a level of competence in Python, a modern programming language currently used in many physics research labs for mathematical modelling. No prior experience is required. The module starts with a grounding in the use of Python and discusses numerical methods. The main focus is then on the ways in which Python can be used for problem solving in physics and astrophysics.

Relationship to other modules

Pre-requisites

BEFORE TAKING THIS MODULE YOU MUST PASS PH2012 AND ( PASS MT2501 AND PASS MT2503 )

Anti-requisites

YOU CANNOT TAKE THIS MODULE IF YOU TAKE PH3082

Assessment pattern

3-hour Computer-based Examination = 75%, continual assessment = 25%

Re-assessment

Oral Re-assessment, capped at grade 7

Learning and teaching methods and delivery

Weekly contact

2hr lab x 10 weeks, 2 x 1hr lecture with Q&A x 10 weeks

Scheduled learning hours

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

The number of hours that students are expected to invest in independent study over the period of the module.

Additional information from school

Aims & Objectives

To experience how numerical modelling is used to explore physical concepts.

To develop a level of expertise in modelling physical problems and to introduce common solving and visualising techniques.

Data analysis to extract physical information from measured data and images.

Solving differential equations numerically.

Learning Outcomes

The students will be able to program in Python, and be able to use Python to solve, visualise and gain insight into a variety of physical problems.

Synopsis

There are introductory exercises teaching basic programming skills in Python, different numerical methods and setting up physical problems. We work through case studies designed to illustrate the use of programming to solve and visualise a variety of physics problems. The case studies can vary from year to year. Past case studies have included: simulating the motion of the planets in the solar system, geometric optics, elastic waves and thermodynamics.

Numerical techniques used in this module include:

Root finding
Studies involving one and two parameters
Model fitting
Parameter optimisation / determining stability regions
Numerical differentiation
Numerical integration
Solving systems of ordinary differential equations

Indicative timetable: weeks 1-2: introduction, weeks 3-5 and 7-11: case studies, each week there will be the opportunity to engage with teaching staff both in-person and online. Indicative deadlines:

Indicative deadlines: Engagement questions: Monday weeks 2-5, and 7-11,

Forum posts: Monday week 7 and Friday week 11.

Additional information on continuous assessment, etc.

The continuous assessment takes the form of forum interactions in Moodle, and engagement questions.

Recommended Books

Please view University online record:

https://sta.rl.talis.com/index.html