PHYSICS 3534 - Computational Physics III
North Terrace Campus - Semester 1 - 2020
General Course Information
Course Code PHYSICS 3534 Course Computational Physics III Coordinating Unit School of Physical Sciences 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 1012 or COMP SCI 1101 or COMP SCI 1102 - other students may apply to the Head of Physics for exemption Incompatible PHYSICS 3000 Assumed Knowledge PHYSICS 2532 Course Description This hands-on course provides an introduction to computational methods in solving problems in physics. It teaches programming tactics, numerical methods and their implementation, together with methods of linear algebra. These computational methods are applied to problems in physics, including the modelling of classical physical systems to quantum systems, as well as to data analysis such as linear and nonlinear fits to data sets. Applications of high performance computing are included where possible, such as an introduction to parallel computing and also to visualization techniques.
Course Coordinator: Professor Derek Leinweber
The full timetable of all activities for this course can be accessed from Course Planner.
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) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
2,5,6,7,8 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
1-8 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
4,6,7,8 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
1-8 Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- Able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
2,3,7,8 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
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
MyUni: Teaching materials and course documentation will be posted on the MyUni website (http://myuni.adelaide.edu.au/).
Learning & Teaching Activities
Learning & Teaching ModesThe Course Content consists of 2 components
High-Performance Fortran Component
- Lectures 24 x 50-minute sessions with two sessions per week
- Workshops 12 x 170-minute sessions with one session per week
- Duration 13 weeks including the optional teaching week
- Lecture 2 x 50-minute session per week for the first 6 weeks of semester.
- Practical Session 1 x 3hr session per week for 8 weeks.
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 SummaryThe course content will include the following:
Introduction to UNIX/Linux
- common UNIX commands and options; the emacs editor
- remote access to computer clusters
- conditional statements
- loops and arrays
- modules, functions and subroutines, scoping of variables
- algebraic simplifications, matrix algebra, symbolic differentiation and integration
- analytical solutions of differential equations
- 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
- 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
Small Group Discovery ExperienceComputational Physics III provides several opportunities for small group discovery experiences. With the leadership of two senior academics, students work together, sharing their experiences, as they develop expertise in solving advanced computational-physics problems. A signature of the course is the development of computational solutions to large complex problems during the workshop components of the course over the duration of a few weeks, typically two 3-hour sessions per week for 3 weeks. With the guidance of two senior academics, students discover advanced coding techniques in generating major-project solutions submitted for assessment in each of the Python and High-Performance Fortran components.
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 Task Task Type Percentage of total assessment for grading purposes Hurdle (Yes/No) Learning Outcome Projects Formative and Summative
No 1-8 (not all projects will assess every objective) Fortran Test Formative and Summative 15% No 1-8 (not all projects will assess every objective) Python Test Summative 20% No 1-8 Written Examination Summative 35% No 1-8
Assessment Related RequirementsTo obtain a grade of Pass or better in this course, a student must attend the examination.
Assessment DetailProjects, Assignments and Tests: (65% of total course grade)
The standard assessment consists of 2 projects and 1 test in the HP-Fortran component and 1 project
and 1 test in the Python 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.
Written Examination: (35% of total course grade)
One exam is given to address understanding of and ability to use the material examined in the HP-Fortran
component of the course.
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.
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 Access Adelaide.
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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