Computational methods are a central aspect of modern science, often providing a bridge between traditional experimental and theoretical approaches to physical science. Computational physics lies at the intersection of Physics, Mathematics, and Computer Science. For research applications it is thus essential to have a sound grasp of how to leverage computational resources to gain insight into the inner workings of complex physical systems.
This course airms to provide the core tools and methodology of computational physics. The emphasis is on gaining practical skills, and on students gaining 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 course is taught in the open-source language Julia, for which prior knowledge is not required. 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.
The course consists of 20 lectures, which are highly integrated with a weekly practical lab session of three hours. The course assignments are worked on during the lab session, and an additional (optional) one hour help session is held each week.
Associate Professor Ashton Bradley
|Topics CoveredLecturer: Associate Professor Ashton Bradley |
|The Julia language and functional programming|
|Computational units and dimensional analysis|
|Solving systems of coupled ordinary equations, linear and nonlinear systems|
|Discretization, partial differential equations|
|Fourier series, Fourier transforms, and spectral analysis|
|Lecturer: Dr Annika Seppälä |
|Real-world applications and Big Data; Introduction to machine learning for Physicists|
|Applications will include: systems of coupled oscillators; heat/diffusion equation; Schrödinger equation; analysis of climate and space weather data|
Formal University Information
The following information is from the University’s corporate web site.
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
- View more information about PHSI 365
- 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.