PHYSICS 3534 - Computational Physics III
North Terrace Campus - Semester 1 - 2024
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General Course Information
Course Details
Course Code PHYSICS 3534 Course Computational Physics III Coordinating Unit Physics Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 7.5 hours per week Available for Study Abroad and Exchange Y Prerequisites PHYSICS 2534, PHYSICS 2510, MATHS 2101 or MATHS 2202, MATHS 2102 or MATHS 2201, COMP SCI 1015 or COMP SCI 1101 or COMP SCI 1102 or ENG 1002 - other students may apply to the Head of Physics for exemption Incompatible PHYSICS 3000 Assumed Knowledge PHYSICS 2532 Assessment Projects, tests, written exam Course Staff
Course Coordinator: Professor David Ottaway
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
- Identify modern programming methods;
- Describe the capabilities and limitations of computational methods in physics;
- Identify and describe the characteristics of various numerical methods;
- Establish tactics for encapsulating and hiding complexity;
- Independently program computers using leading-edge tools;
- Formulate and solve computationally a selection of problems in physics;
- Use the tools, methodologies, language and conventions of physics to test and communicate ideas and explanations;
- Resolve the appropriate paradigm for addressing current computational physics challenges.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
2,5,6,7,8 Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
1-8 Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
4,6,7,8 Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
1-8 Attribute 5: Intercultural and ethical competency
Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.
2,3,7,8 Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
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Learning Resources
Required Resources
Extensive Lecture Notes and Workshops will be provided (Links to an external site.)
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Recommended Resources
Modern Fortran Explained (4th edition), by Michael Metcalf, John Reid, Malcolm Cohen (Oxford)
Fortran 95/2003 Explained, Metcalf, Reid and Cohen (Oxford)
Fortran 90/95 Explained, Metcalf and Reid (Oxford)Fortran 90/95 for Scientists and Engineers, Chapman (McGraw-Hill Higher Education)
Fortran 90 Programming, Ellis, Philips and Lahey (Addison-Wesley)
Numerical Recipes in FORTRAN: The Art of Scientific Computing, Press, et al. (Cambridge University Press)
Computational Physics - Fortran Version, Koonin and Meredith (Addison Wesley).
"Mastering Matlab " by Duane C. Hanselman and Bruce L. Littlefield, Prentice Hall, 2012
Online Learning
MyUni: Teaching materials and course documentation will be posted on the MyUni website ().
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Learning & Teaching Activities
Learning & Teaching Modes
The Course Content consists of 2 components in which practical components are interlaced with discussion on code development
Python Component
- Workshops 14 x 170-minute sessions with two session per week
- Workshops 10 x 170-minute sessions with two session per week
Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
A student enrolled in a 3 unit course, such as this, should expect to spend, on average 12 hours per
week on the studies required. This includes both the formal contact time required to the course (e.g.,
lectures and practicals), as well as non-contact time (e.g., reading and revision).Learning Activities Summary
The course content will include the following:
Introduction to UNIX/Linux
- common UNIX commands and options; the emacs editor
- remote access to computer clusters
Programming
- conditional statements
- loops and arrays
- modules, functions and subroutines, scoping of variables
Symbolic computation
- algebraic simplifications, matrix algebra, symbolic differentiation and integration
- analytical solutions of differential equations
Numerical methods
- numerical integration, transformation of variables
- Monte Carlo methods
- finite element methods
Solving Differential equations
- ordinary and partial differential equations, initial value problems, boundary value problems
- Taylor expansion method, Runge-Kutta method
- local and accumulated truncation errors, error control
Modelling
- trajectories and particle motion, linear and nonlinear initial value problems
- Schrodinger equation
- normalization of wave functions, energy levels, orthogonality of wave functions, expectation values, probability calculations
- Monte-Carlo based Markov-Chain techniques for simulating the Ising spin model in statistical mechanics
- problems in electromagnetism and solution by finite elements
- interpolation, interpolating polynomials, errors
- curve fitting and best fits using linear and non-linear least-squares fits
- inverting matrices -
Assessment
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
Assessment Task Task Type Percentage of total assessment for grading purposes Hurdle (Yes/No) Learning Outcome Projects Formative and Summative 55%
No 1-8 (not all projects will assess every objective) Python Tests Formative and Summative 30% No 1-8 (not all projects will assess every objective) C++ Tests Summative 15% No 1-8 Assessment Related Requirements
To obtain a grade of Pass or better in this course, a student must attain a minimum of 40% for the final examination. Students who attain a final course grade of at least 45% but do not attain a minimum of 40% for the exam may be offered an Additional
Academic Exam during the Replacement/Additional Assessment period, in line with the Modified Arrangements for Coursework Assessment Policy.Assessment Detail
Projects, Assignments and Tests: (100% of total course grade)
The standard assessment consists of 2 projects and 2 tests in the Python component and 1 project
and 1 test in the C++ component. This may be varied by negotiation with students at the start of the
semester. This combination of projects, tests and summative assignments is used during the semester to
address understanding of and ability to use the course material and to provide students with a
benchmark for their progress in the course.
Submission
If an extension is not applied for, or not granted then a penalty for late submission will apply. A penalty of 10% of the value of the assignment for each calendar day that the assignment is late (i.e. weekends count as 2 days), up to a maximum of 50% of the available marks will be applied. This means that an assignment that is 5 days late or more without an approved extension can only receive a maximum of 50% of the marks available for that assignment.
Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through .
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Student Feedback
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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Fraud Awareness
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.
The 成人大片 is committed to regular reviews of the courses and programs it offers to students. The 成人大片 therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.
