Overview
Computational methods for solving physics problems. Graphical visualisation. Numerical techniques for solving classes of equations in a variety of physical examples. Curve fitting, Fourier transforms. Non-linear dynamics and chaos.
This paper aims to provide the core tools and methodology of computational physics. The emphasis is on gaining practical skills, and a key objective is that students gain the techniques and the confidence to tackle a broad range of problems in physics. Topics have been selected to provide a broad basis of skills, and each is illustrated by application to physical systems. The paper is taught in the open-source language Julia, for which prior knowledge is not essential. The language will feel very familiar to those with Matlab or Python experience and provides a flexible and powerful platform for modern technical computing and a convenient, open science environment.
About this paper
| Paper title | Computational Physics |
|---|---|
| Subject | Physics |
| EFTS | 0.15 |
| Points | 18 points |
| Teaching period | Semester 1 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,243.65 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- (36 200-level PHSI points or (18 200-level PHSI points and 18 200-level MATH points)) and MATH 140
- Schedule C
- Science
- Contact
- ashton.bradley@otago.ac.nz
- More information link
- View more information about PHSI 365
- Teaching staff
Course Co-ordinator: Associate Professor Ashton Bradley
- Textbooks
Textbooks are not required for this paper.
- Graduate Attributes Emphasised
- Global perspective, Interdisciplinary perspective, Lifelong learning, Scholarship, Communication, Critical thinking, Information literacy, Self-motivation.
View more information about Otago's graduate attributes. - Learning Outcomes
After completing this paper students will be able to:
- Understand and apply the basic methodology of computational physics to a broad range of physics problems
- Write well-structured Julia programmes and independently acquire additional coding skills
- Process, analyse and plot data from a variety of physical phenomena and interpret their meaning
- Use specific computational techniques to solve ordinary differential equations and systems of linear equations, to analyse and manipulate spectral content of digitised data
- Present well-structured reports of the results of computational investigations in an open science framework