68416 Computational Physics
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Subject handbook information prior to 2020 is available in the Archives.
Credit points: 6 cp
Result type: Grade and marks
Requisite(s): 33360 Mathematics for Physical Science OR 68038 Advanced Mathematics and Physics
These requisites may not apply to students in certain courses. See access conditions.
Description
This subject introduces the key elements of computational physics such as methods for solving physical problems numerically and the use of computers for simulating the dynamics of large or complex systems. Depending on selected project topics, numerical techniques including matrix manipulation, iterative optimisation and differential equation solvers may be introduced. These are developed and applied to selected problems in areas such as quantum mechanics, statistical mechanics, electrodynamics and molecular dynamics. Project work allows students to explore advanced simulations and further develop analysis and visualisation of results.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Write programs in a numerical programming environment to solve simple equations, analyse results and plot graphs |
|---|---|
| 2. | Write and document code in a manner that makes it re-usable and transferable. |
| 3. | Build models of relatively complex physical systems, by researching the required physics, and constructing, testing and applying appropriate simulation modules. |
| 4. | Communicate results and information in a professional and competent way appropriate to the discipline. |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of following course intended learning outcomes:
- Apply: Develop experimental skills in established and emerging applied physics techniques in research contexts. (1.1)
- Analyse: Examine and utilise in-depth technical knowledge of applied physics as well as relevant areas in applied mathematics, nanotechnology and materials science. (1.2)
- Synthesise: Apply analytical and computational techniques to analyse and suggest solutions to solve applied physics problems. (1.3)
- Apply: Analytically predict the behaviour of real-world systems by using physical models with underlying assumptions, and suggested demonstrations and experiments. (2.1)
- Synthesise: Tackle the challenge of real-world problems by identifying the underlying physics and critically evaluating different solutions with consideration of uncertainties arising from experimental investigations. (2.3)
- Analyse: Develop technical, practical, and professional skills to address applied physics problems using time management, personal organisation, and collaborative skills. (3.2)
- Synthesise: Exhibit competence in using scientific tools to display, process, and analyse data from instrumentation to make positive and ethical contributions to society. (3.3)
- Apply: Demonstrate individual and independent learning strategies enabled by peer review and self-reflection. (4.1)
- Analyse: Dissect new information acquired through experimentation to formulate creative hypotheses. (4.2)
- Apply: Prepare and deliver presentations on topics in applied physics and research outcomes to different audiences using a variety of media. (5.1)
- Analyse: Construct a report on a research investigation, demonstrating understanding of theoretical concepts, appropriate graphical literacy, and interpretation of results. (5.2)
- Synthesise: Extend professional interpersonal communication skills to peers. (5.3)
Contribution to the development of graduate attributes
1. Disciplinary knowledge and its appropriate application
Students will develop knowledge of key numerical algorithms through exercises at the beginning of the subject, reading resources and applying these algorithms to basic physical processes. They further extend their knowledge of algorithms and physics as they develop computer models for their specific project. Careful comparison to test cases and feedback will inform this learning process.
2. An Inquiry-oriented approach
Students will systematically explore computational problems, particularly in addressing their project, and by doing so develop an understanding of the benefits and limitations of a computational approach. Ongoing review and feedback will assist students in development.
3. Professional Skills and their appropriate application
Students will develop basic programming skills (e.g. in Matlab) through structured exercises and extend these skills throughout their project, with development guided by regular feedback and review. They will be shown how to document their work, and come to understand its value through feedback and peer-review. They have the opportunity to collaborate, and learn the importance of appropriate attribution, through active participation in projects and review. They will be informed of management processes and develop these through planning and ongoing review of their progress.
4. The ability to be a Lifelong Learner
Students will develop in at least two important areas: driving their own learning during their project, and critical evaluation of their own and others work.
6. Communication Skills
Students will develop communication skills throughout this subject, especially of graphical information but also of documentation and appropriately structured reporting. Ongoing participation in review with evaluation and action in response to feedback is particularly important in this regard.
7. Initiative and innovative ability
Students take the initiative in selecting their project and are responsible for the direction of the investigation. Through ongoing review of their own and other's work they can evaluate a variety of approaches and have the opportunity to try approaches they hadn't previously considered.
Teaching and learning strategies
2x2hr mixed lecture & computer lab sessions will be available. We will only make minimal use of lectures as necessary, and will focus on engaging in active learning and consultation. You are expected to be proactive in managing your learning and will need to spend some time outside of class, so that you can make the best use of consultation opportunities in class.
The overarching strategy is producing a portfolio of work that demonstrates a solution to a problem of your choice, which has been developed and tested throughout the session with regular review by yourself, peers and tutors. You have the opportunity to work collaboratively as a team (typically as a pair), and are encouraged to consult with other teams and tutors to review the quality of your work. Evaluating and acting on this feedback will help you improve the quality of your work. You will be supported to develop programming, project management, and communication skills through a number of activities with ongoing review.
Initially you will assess and develop your personal competence in basic programming and numerical principles by completing basic exercises before you come to class, and then extending these skills in class in consultation with your peers and tutors. Resources including notes, tutorials and self-assessments will be linked through UTSonline. It is recommended that you review your progress on specific skills at least weekly, especially if you have no prior programming experience. Ideally as you progress you will develop a short report on implementation and testing of a basic algorithm, to practice a formal review by around census.
You will choose a project topic and develop a plan with minimum and stretch targets and map out milestones for their completion, ideally by around census but no later than the mid-session break. You should consult with a tutor before enacting this plan, and continue to review progess and update accordingly on a regular basis (e.g. weekly).
You will develop and test computer code that enables you to explore your topic - it is expected that as you become more proficient at programming you will take control of your learning in this area. You should aim to have a basic code working soon after the break and then seek feedback on a documented test demonstration. Peers and tutors will continue to be a useful source of advice, especially on your exploration of the topic. You should submit your draft report at least two weeks prior to the due date so that you have adequate time for review against the rubric and undertaking revisions.
As you progress, you will also continuously develop your professional communication as you and your colleagues review your work.
Assessment
Assessment task 1: Project
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2, 3 and 4 This assessment task contributes to the development of course intended learning outcome(s): 1.3, 2.3, 3.3, 4.2 and 5.2 |
|---|---|
| Groupwork: | Group, individually assessed |
| Weight: | 75% |
| Criteria: | The quality of the submission (both presentation and the outcomes it demonstrates) will be assessed using a rubric. The submission should address the specified graduate attributes and subjective objectives as detailed in the rubric. The submission should: |
Assessment task 2: Programming assignments
| Objective(s): | This assessment task addresses subject learning objective(s): 1 and 2 This assessment task contributes to the development of course intended learning outcome(s): 1.1 and 5.1 |
|---|---|
| Groupwork: | Individual |
| Weight: | 15% |
| Criteria: | Program gives correct result Code appropriately documented Results appropriately presented |
Assessment task 3: Mini report & review
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2 and 4 This assessment task contributes to the development of course intended learning outcome(s): 1.2, 2.1, 3.2, 4.1 and 5.3 |
|---|---|
| Groupwork: | Individual |
| Weight: | 10% |
| Criteria: | Mini Report (half of marks for this assessment) Rubric criteria except for motivation & review, with particular emphasis on Methods (validated code) & Results. Meta Review (half of marks for this assessment) The feedback given addresses the relevant criteria. The feedback given outlines "what" and "how" to improve. |
Minimum requirements
Any assessment task worth 40% or more requires the student to gain at least 40% of the mark for that task. If 40% is not reached, an X grade fail may be awarded for the subject, irrespective of an overall mark greater than 50.
You must complete and submit the programming assignments to be eligible to pass this subject.
Recommended texts
Getting started with Matlab:
http://www.mathworks.com/access/helpdesk/help/pdf_doc/matlab/getstart.pdf
Other material will be supplied or suggested as needed (e.g. via UTSonline)