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Outline:

This module aims to provide students with an understanding of modern techniques and approaches in computational physics, and how physical problems can be modelled in practice. Students will gain skills in a modern, structural programming language (Python3) and the principles of coding. Analytical and mathematical results underlying approaches to computational physics will be understood, and applied to different areas of physics. Students will be able to start from the statement of a simple physical problem, and create a computer program to model the problem and explore the physics related to it. The module will provide students with a deep understanding of the strengths and weaknesses of numerical simulations in physics.

Aims:

• To introduce physicists to the techniques and approaches of computational physics
• To teach a modern, structural programming language (Python3) and principles of coding
• To show how the mathematical techniques introduced in PHAS0026 are implemented in practice
• To provide students with a deep understanding of the strengths and weaknesses of numerical simulations

Intended Learning Outcomes:

Students will acquire an initial understanding of the area of computational physics, suitable for further advanced study in this area.Ìý They will gain a good expertise in the Python3 language, as well as understanding the analytical and mathematical results underlying approaches to computational physics.Ìý They will be able to start from the statement of a simple physical problem, and create a computer program to model the problem and explore the physics related to it.

Teaching and Learning Methodology:

The module is based on interactive sessions formed from a mixture of lectures and practical problem solving and programming.

In addition to timetabled sessions, it is expected that students engage in self-study in order to master the material. This can take the form, for example, of practicing example questions and further reading in textbooks and online.

Indicative Topics:

Arrays, loops and functions; optimisation and root finding; integration and differentiation; ordinary differential equations; partial differential equations; modelling with particles; random numbers and stochastic methods; integral transforms.