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PHSI365 Computational Physics

Paper Description

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 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 course 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.

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.

Assessment:
Final exam 50%, Assignments 50%.

Important information about assessment for PHSI365

Course Coordinator:
Dr Ashton Bradley

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 its 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
Topics CoveredLecturer: Dr 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
Lecturer: Dr Annika Seppälä
Fourier series, Fourier transforms, and spectral analysis
Stochastic methods
Real-world applications and Big Data
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.

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Details

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 0.1500
Points 18 points
Teaching period First Semester
Domestic Tuition Fees (NZD) $1,059.15
International Tuition Fees (NZD) $4,627.65

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Prerequisite
(36 200-level PHSI points or (18 200-level PHSI points and 18 200-level MATH points)) and MATH 170
Schedule C
Science
Contact
ashton.bradley@otago.ac.nz
Teaching staff

Course Co-ordinator: Dr Ashton Bradley
Dr Annika Seppälä

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:
  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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Timetable

First Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Blackboard

Lecture

Stream Days Times Weeks
Attend
L1 Tuesday 13:00-13:50 9-16, 18-22
Thursday 11:00-11:50 9-16, 18-22

Practical

Stream Days Times Weeks
Attend
P1 Friday 14:00-17:50 9-15, 18-22

Tutorial

Stream Days Times Weeks
Attend
T1 Tuesday 11:00-11:50 9-16, 18-22

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 0.1500
Points 18 points
Teaching period First Semester
Domestic Tuition Fees Tuition Fees for 2020 have not yet been set
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

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Prerequisite
(36 200-level PHSI points or (18 200-level PHSI points and 18 200-level MATH points)) and MATH 170
Schedule C
Science
Contact
ashton.bradley@otago.ac.nz
Teaching staff

Course Co-ordinator: Dr Ashton Bradley
Dr Annika Seppälä

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:
  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.

^ Top of page

Timetable

First Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Blackboard

Lecture

Stream Days Times Weeks
Attend
L1 Tuesday 13:00-13:50 9-15, 17-22
Thursday 11:00-11:50 9-15, 17-22

Practical

Stream Days Times Weeks
Attend
P1 Friday 14:00-17:50 9-14, 17-22

Tutorial

Stream Days Times Weeks
Attend
T1 Tuesday 11:00-11:50 9-15, 17-22