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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 Phython experience and provides a flexible and powerful platform for modern technical computing and a convenient, open science environment.
|Paper title||Computational Physics|
|Teaching period||Semester 1 (On campus)|
|Domestic Tuition Fees (NZD)||$1,110.75|
|International Tuition Fees||Tuition Fees for international students are elsewhere on this website.|
- (36 200-level PHSI points or (18 200-level PHSI points and 18 200-level MATH points)) and MATH 140
- Schedule C
- More information link
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- Teaching staff
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.