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PHSI365 Computational Physics

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
Paper code PHSI365
Subject Physics
EFTS 0.15
Points 18 points
Teaching period Semester 1 (On campus)
Domestic Tuition Fees (NZD) $1,141.35
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

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(36 200-level PHSI points or (18 200-level PHSI points and 18 200-level MATH points)) and MATH 140
Schedule C
Teaching staff

Course Co-ordinator: Dr Ashton Bradley
Dr Annika Seppälä


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:
  1. Understand and apply the basic methodology of computational physics to a broad range of physics problems.
  2. Write well-structured Julia programmes and independently acquire additional coding skills.
  3. Process, analyse and plot data from a variety of physical phenomena and interpret their meaning.
  4. Use specific computational techniques to solve ordinary differential equations and systems of linear equations, to analyse and manipulate spectral content of digitised data.
  5. Present well-structured reports of the results of computational investigations in an open science framework.

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Semester 1

Teaching method
This paper is taught On Campus
Learning management system


Stream Days Times Weeks
A1 Tuesday 13:00-13:50 9-14, 16, 18-22
Thursday 11:00-11:50 9-14, 16-22


Stream Days Times Weeks
A1 Friday 14:00-16:50 9-13, 16-22


Stream Days Times Weeks
A1 Tuesday 11:00-11:50 9-14, 16, 18, 20-22