Course Catalogue

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This course will include further learning about computational physics at a level that is accessible to Physics Msci students, which will include reading and computational exercises on basic topics (libraries, Git, multivariate fitting, machine learning and AI basics) followed by a longer piece of project work applying some of the techniques to suitable datasets and problems.

6 hours nominal learning per week in Semester 2

Week 22: introductory lecture giving overview of the course and a brief outline of the content of the labs in Weeks 6-8

Weeks 22-24: one 3 hour lab session provided one afternoon a week with teaching staff available to discuss exercises and the project work - students are expected to do more work in their own time

Weeks 25-32: independent work on a longer project-style assignment, with the 3 hour lab session available so students can work there or drop in and ask questions. The project work will have a small number of different projects supervised by different staff members, with each being taken by a group of students, who will meet with the relevant supervisor in the lab on a regular basis, but will ultimately be responsible for completing their assignment independently.

Interim talks in week 30 with questions (assessed)

Final report due in last week of teaching in Semester 2 (week 32)

PHYS4009 Honours Physics Laboratory

PHYS4008 Honours Computational Physics Laboratory and PHYS4029P Theoretical Physics Group Project.

The initial 3 weeks will have hand-ins of laboratory records of their work on the teaching laboratory components in the form of answers to well-defined questions and challenges (5% for each). Formative feedback will be given.

The longer piece of project-style work will be assessed by supervisor assessment of progress (30%) (including assessment of the code base and commit history in git), teaching staff assessment of a talk halfway through (20%) and an assessment of the final report (35%).

To train MSci Physics students in the use of more advanced computing (such as code libraries, complex fitting and machine learning) from an applied perspective in the form of a few assessesd lab exercises accompanied by four lectures outlining some key concepts and tools, followed by their application in a project assessed on the basis of project performance as observed by one of the teachers/demonstrators (with assistance of looking at the GIT commit history), an interim talk to the teachers/demonstrators and their cohort, and a final written formal report.

By the end of this course students will be able to:

■ Use 3rd-party code libraries and understand how to use the documentation within to guide usage

■ Understand how to maintain their own codebase in a git repository

■ Fit complex functions of multiple variables to data using computational tools

■ Appreciate the essential principles of machine learning and some of the main classes and approaches

■ Have a basic understanding of generative AI and LLMs

■ Use some of these skills in performing a longer piece of project-style work on real physics datasets

■ Reinforce previously learned skills on report writing and oral presentation

■ Improve their abilities in the graphical presentation of data using common python libraries