APP MTH 7048 - Applied Mathematics Topic A
North Terrace Campus - Semester 1 - 2019
General Course Information
Course Code APP MTH 7048 Course Applied Mathematics Topic A Coordinating Unit School of Mathematical Sciences Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Available for Study Abroad and Exchange Y Course Description Please contact the School of Mathematical Sciences for further details, or view course information on the School of Mathematical Sciences web site at http://www.maths.adelaide.edu.au
Course Coordinator: Professor Anthony Roberts
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesIn 2019, the topic of this course is Modelling Emergent Dynamics in Complex Systems.
Much of the world around us evolves so that coherent (stripes on a tiger, or quasi-stationary distributions) or incoherent (turbulence) patterns emerge over time. We seek to find ways to characterise, to model, the coherent or incoherent behaviour that we see. What is the aggregate behaviour? How can the whole be more than the sum of its parts? This course will explore how long lasting dynamics emerge after the rapid exponential decay of transients. We find that coordinate transforms clearly separate transients from long-lasting dynamics, even stochastic. But 'long lasting' and 'rapid transient' are subjective decisions to take depending upon application. Consequently, we are empowered to magically model across a multi-grid network, and computer algebra handles the algebraic complexity. Starting from basic asymptotic perturbation methods, this course establishes theory and techniques of dimensional reduction for dynamical systems, and develops how these are applied in modelling dynamics in various scenarios. The detailed syllabus will be chosen interactively with students to reflect student projects and interests.
Assumed knowledge: Modelling with ODEs; PDEs & Waves is useful; linear algebra.
On successful completion of this course students will be able to
- use deep discipline knowledge of mathematical modelling to create asymptotic solutions;
- critically invoke theory and techniques of dimensional reduction for modelling to explore and solve problems in dynamical systems.
- interpret and communicate the modelling and analysis of systems.
- use paradoxes in modelling to become aware of subjectivity in modelling.
- develop knowledge of dynamics on networks and its potential implication for social networks.
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)
all 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
all 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
all 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
Required ResourcesAccess to the intranet.
- A. J. Roberts. Model emergent dynamics in complex systems. SIAM, Philadelphia, Jan 2015.
Online LearningThis course uses MyUni exclusively for providing electronic resources, such as lecture notes, assignment papers, and sample solutions. Students should make appropriate use of these resources.
Learning & Teaching Activities
Learning & Teaching ModesThis course relies on lectures and exercises as the primary learning mechanism for the material. A sequence of written and/or online assignments provides assessment opportunities for students to gauge their progress and understanding.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload Hours Lecture classes 30 100 Assignments/assessment 7 56 Total 156
Learning Activities Summary
- basic asymptotic perturbation methods,
- theory and techniques of dimensional reduction for dynamical systems, and
- applications in modelling dynamics on a continuum and on network hierarchies.
Specific Course RequirementsNone.
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.
Component Weighting Objective Assessed Assignments 30% all Exam 70% all
Assessment Related RequirementsAn aggregate score of at least 50% is required to pass the course.
Assessment item Distributed Due date Weighting Assignment 1 week 2 week 3 4% Assignment 2 week 4 week 5 4% Assignment 3 week 6 week 7 4% Assignment 4 week 8 week 9 4% Assignment 5 week 10 week 11 4% Assignment 6 week 12 week 13 4% Homework/Project ongoing ongoing 6%
SubmissionAssignments and projects must be left in the course hand-in box or given to the lecturer in person by the specified deadline. Failure to meet the deadline without reasonable and verifiable excuse may result in a significant penalty.
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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