Module Details

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Computational Modelling
Code PHYS305
Coordinator Dr J Kretzschmar
Year CATS Level Semester CATS Value
Session 2021-22 Level 6 FHEQ Second Semester 15


• To revise Python programming skills and reinforce object-oriented concepts and methods of a high-level Object-oriented programming language.

• To apply Python for the computational modelling of physical phenomena and solution of complex physics problems using Monte Carlo techniques and numerical integration.

• To further develop the ability to efficiently implement algorithms using Python and verify the results.

• To give students experience of working independently and in small groups on an original problem.

• To give students an opportunity to display the high quality of their work, initiative and ingenuity.

• To give students experience of report writing displaying high standards of composition and production.

• To give an opportunity for students to further develop and display oral communication skills.

Learning Outcomes

(LO1) Acquire a deep knowledge of a high level programming language including object-oriented elements.

(LO2) Gain experience how to apply computational methods to the solution of physics problems, including the set up of a complex model of physical phenomena or experimental situation

(LO3) Experience in researching literature and other sources of relevant information

(LO4) Experience in testing model against data from experiment or literature

(LO5) Improved ability to organise and manage time.

(LO6) Improved skills in report writing.

(LO7) Improved skills in explaining project under questioning.

(S1) Problem solving skills

(S2) Teamwork

(S3) Organisational skills

(S4) Communication skills

(S5) IT skills



• Week 1: Introduction into the module; Revision of the Python programming techniques studied in Computational Physics (Phys205), including data structures, program flow, data input/output and plotting methods; Introduction of object-oriented concepts and application in an example with Lorentz Four-vectors and relativistic calculations

• Week 2: Revision of Monte Carlo Techniques: integration problems, random number generation (such as Gaussian or exponential distributions), application to simple physics problems; data handling and visualisation through histograms

• Week 3: Numerical solutions to differential equations with Euler and Runge-Kutta methods; application and verification of numerical solutions to the propagation of charged particles in electromagnetic fields

• Week 4: Construction of an object-oriented framework to construct experiments with particle tracking, simulated detection and reconstruction

• Week 5: Intr oduction of realistic interactions of particles with matter, implementation of effects of energy loss through ionisation and multiple scattering in the framework

• Week 6 - 10: Students will form groups of 3 to 4 and choose a project from a list suggested by the Module Organiser or prepared in discussion with the Module Organiser. Projects will be chosen to match the student's particular interests as far as possible. Details of the project aims will be decided in discussions between the student and the supervisor. An electronic logbook will be kept by the students to aid the team communication and improve possibilities to interact between students and supervisor. There will be regular meetings between the students and the supervisors to track the progress.

• The Projects are assessed by oral presentations, normally taking place in Week 11 of Semester 2, as well as a joint project report, normally to be handed in before the end of Week 11 of Semester 2. Th e electronic logbook will form a part of the assessment.

Teaching and Learning Strategies

Teaching Method 1 - Lecture [online]
Description: 1 hour lectures to introduce students to weekly exercises
Attendance Recorded: Yes
Notes: 6 x 1 hour lectures

Teaching Method 2 - Laboratory Work
Description: Computing based activities
Attendance Recorded: Yes
Notes: 5 x 8 hours problems classes, 5 x 9 hours project work

The module will be delivered remotely in 2021. Asynchronous learning materials (notes/videos/exercises etc) will be made available to students through the VLE. The module will have regular synchronous sessions in active learning mode.
We are planning no changes to module content compared to previous years, and expect students to spend a similar amount of time-on-task compared to previous years. These changes will mainly constitute a rebalancing of hours from scheduled directed learning hours to unscheduled directed learning hours as students will have some flexibility as to when they access asynchronous materials.

Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours           85


Timetable (if known)              
Private Study 59


EXAM Duration Timing
% of
Penalty for late
CONTINUOUS Duration Timing
% of
Penalty for late
Individual Project Report Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :2  Each students write     30       
Group Project Report Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :2  Groups of 3-4 studen    20       
Individual Presentation. Standard UoL penalty applies for late submission, This is not an anonymous assessment. Assessment Schedule (When) :2 Each student prepares a 5-7-minute contribution t  5-7 minutes contribu    30       
5 exercises (weeks 1-5) Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :2  5 exercises (weeks 1    20       

Recommended Texts

Reading lists are managed at Click here to access the reading lists for this module.