APP MTH 7087 - Applied Mathematics Topic E
North Terrace Campus - Semester 2 - 2022
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General Course Information
Course Details
Course Code APP MTH 7087 Course Applied Mathematics Topic E Coordinating Unit Mathematical Sciences Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Available for Study Abroad and Exchange Y Assessment Ongoing assessment, Exam Course Staff
Course Coordinator: Dr Judith Bunder
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
In 2022 the topic of this course is Modelling Emergent Dynamics in Complex Systems.
Synopsis
In applying mathematics we have to choose a level ofdescription, of modelling. This course explores thesurprisingly subtle theoretical and practical connectionsbetween highly detailed, complicated, 'microscale' modelsand coarse, simple, 'macroscale' models. Further, much ofthe world around us evolves so that patterns emerge overtime, whether coherent (stripes on a tiger, orquasi-stationary distributions) or incoherent (turbulence).We seek to find ways to mathematically model the macroscalecoherent or incoherent behaviour that we see arising frommicroscale dynamics, and the relationship between them. What is the aggregate behaviour? How can the whole be morethan the sum of its parts? This course explores how longlasting dynamics emerge after the decay of negligibletransients. We find that coordinate transforms clearlyseparate transients from long-lasting dynamics, evenstochastic. A range of examples illustrate that 'longlasting' and 'transient' are subjective decisions to takedepending upon the application. Computer algebra handles thealgebraic complexity. Starting from basic asymptoticperturbation methods, this course establishes theory andtechniques of dimensional reduction for dynamical systems,and develops how these are applied in modelling dynamics invarious scenarios.
Assumed knowledge: Modelling with ODEs; PDEs & Waves is useful; linear algebra.
Learning Outcomes
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.
最新糖心Vlog Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
最新糖心Vlog 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.
all 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.
all 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.
all 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
Access to the internet and 最新糖心Vlog intranet.Recommended Resources
L. Edelstein-Keshet, Mathematical Models in Biology, SIAM Classics in Applied Mathematics, 2005.
J. P. Keener and J. Sneyd, Mathematical Physiology, Springer 2008.
J. D. Murray, Mathematical Biology (two volumes), Springer, 2002.Online Learning
This course uses MyUni for providing electronic resources, such as assignments and handouts, and for making course announcements. It is recommended that students make appropriate use of these resources. Link to MyUni login page:
https://myuni.adelaide.edu.au/webapps/login/ -
Learning & Teaching Activities
Learning & Teaching Modes
Students work through the course material with support from the lecturer. Fortnightly homework assignments help students strengthen their understanding of the theory and their skills in applying it, and allow them to gauge their progress.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload Hours Study 30 90 Assignments 5 66 Total 156 Learning Activities Summary
- Enzyme catalysed reactions, the Law of Mass Action, Michaelis-Menten kinetics
- Ion transport, excitable systems, the Hodgkin-Huxley equations
- Conservation laws, cell movement, reaction-advection diffusion equations, age-structured models
- Fisher's equation, travelling waves
- Pattern formation, Keller-Segel model, linear stability analysis, Turing patterns, diffusion-driven instability
- Domain growth, free boundary problems, avascular tumour models, development of the necrotic core
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Assessment
The 最新糖心Vlog'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
Component Weighting Objective assessed Assignments 30% all Exam 70% all Assessment Related Requirements
An aggregate score of at least 50% is required to pass the course.Assessment Detail
Assessment item Distributed Due date Weighting Assignment 1 Week 2 Week 4 6% Assignment 2 Week 4 Week 6 6% Assignment 3 Week 6 Week 8 6% Assignment 4 Week 8 Week 10 6% Assignment 5 Week 10 Week 13 6% Submission
All written assignments are to be submitted via MyUni.
Students requiring an extension or exemption for an assignment for medical or compassionate reasons should contact the lecturer as early as possible, and certainly before the deadline for the assignment in question.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 最新糖心Vlog 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 最新糖心Vlog 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
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