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Unit outline_

MATH3978: PDEs and Waves (Advanced)

Semester 2, 2020 [Normal day] - Camperdown/Darlington, Sydney

The aim of this unit is to introduce some fundamental concepts of the theory of partial differential equations (PDEs) arising in Physics, Chemistry, Biology and Mathematical Finance. The focus is mainly on linear equations but some important examples of nonlinear equations and related phenomena re introduced as well. After an introductory lecture, we proceed with first-order PDEs and the method of characteristics. Here, we also nonlinear transport equations and shock waves are discussed. Then the theory of the elliptic equations is presented with an emphasis on eigenvalue problems and their application to solve parabolic and hyperbolic initial boundary-value problems. The Maximum principle and Harnack's inequality will be discussed and the theory of Green's functions.

Unit details and rules

Academic unit Mathematics and Statistics Academic Operations
Credit points 6
Prerequisites
? 
A mark of 65 or greater in 12 credit points of MATH2XXX units of study
Corequisites
? 
None
Prohibitions
? 
MATH3078 or MATH4078
Assumed knowledge
? 

[MATH2X61 and MATH2X65] or [MATH2X21 and MATH2X22]

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Daniel Hauer, daniel.hauer@sydney.edu.au
Lecturer(s) Daniel Hauer, daniel.hauer@sydney.edu.au
Type Description Weight Due Length
Final exam (Open book) Type C final exam Exam
Written exam
60% Formal exam period 2 hours
Outcomes assessed:
Tutorial quiz Quiz 1
Quiz (short take-home test)
10% Mid-semester break
Due date: 06 Oct 2020 at 08:00

Closing date: 06 Oct 2020
45 minutes
Outcomes assessed:
Assignment Video assignment 1
Video assignment
10% Week 04
Due date: 20 Sep 2020 at 23:59
3 minutes
Outcomes assessed:
Assignment Mathematical project
report & in-class presentation on a topic related to the unit of study.
10% Week 09
Due date: 01 Nov 2020 at 23:59
10 pages and 1 presentation
Outcomes assessed:
Tutorial quiz Quiz 2
Quiz (short take-home test)
10% Week 12
Due date: 17 Nov 2020 at 08:00

Closing date: 17 Nov 2020
45 minutes
Outcomes assessed:
Type C final exam = Type C final exam ?

Assessment summary

  • Video assignment: The video assignment assesses one specific topic discussed in the proceeding lecture in form of solving a given problem. It requires to produce a short 3 minutes video with the webcam or mobile phone recording the presentation on how to solve the given problem.
  • Quizzes: In quiz 1 the content taught in the weeks 1-6 will be assessed and in quiz 2 the content of the weeks 7-12. For each quiz the material discussed in the lecture notes, and all mathematical problems discussed in the tutorials of these weeks are to be assessed. A sample quiz from the year before will be provided.
  • Mathematical project: The student needs to write an essay about a special topic which is related to this unit of study and present her/his results in front of the class.

The better mark principle will be used for the quiz so do not submit an application for Special Consideration or Special Arrangements if you miss a quiz. The better mark principle means that the quiz counts if and only if it is better than or equal to your exam mark. If your quiz mark is less than your exam mark, the exam mark will be used for that portion of your assessment instead.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

Representing complete or close to complete mastery of the material;

Distinction

75 - 84

Representing excellence, but substantially less than complete mastery;

Credit

65 - 74

Representing a creditable performance that goes beyond routine knowledge

and understanding, but less than excellence;

Pass

50 - 64

Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Introduction into PDEs - What is a PDE? The four most important PDEs and classes of PDEs. Lecture (3 hr)  
Review on ODEs Lecture (1 hr)  
Week 02 The principle of Superposition and classification of 2nd-order linear PDEs. Lecture (3 hr)  
Introduction into PDEs - What is a PDE? The four most important PDEs and classes of PDEs. Tutorial (1 hr)  
Week 03 1st-order linear PDEs and the method of characteristics Lecture (3 hr)  
The principle of Superposition and classification of 2nd-order linear PDEs. Tutorial (1 hr)  
Week 04 Conservation laws, standing waves, traveling waves, waves trains, and general transport equations with uniform velocity vector. Lecture (3 hr)  
1st-order linear PDEs and the method of characteristics Tutorial (1 hr)  
Week 05 Linear and nonlinear transport equations Lecture (3 hr)  
Conservation laws, standing waves, traveling waves, waves trains, and general transport equations with uniform velocity vector. Tutorial (1 hr)  
Week 06 Shock waves Lecture (3 hr)  
Linear and nonlinear transport equations Tutorial (1 hr)  
Week 07 Laplace's equation on various symmetric regions in the plane and space, and the fundamental solution. Lecture (3 hr)  
Shock Waves Tutorial (1 hr)  
Week 08 Harmonic functions, mean-value property, maximum principles, and Harnack's inequality Lecture (3 hr)  
Laplace's equation on various symmetric regions in the plane and space, and the fundamental solution. Tutorial (1 hr)  
Week 09 Sturm-Liouville operator on a bounded interval and eigenvalue problems. Lecture (3 hr)  
Harmonic functions, mean-value property, maximum principles, and Harnack's inequality Tutorial (1 hr)  
Week 10 The Schrödinger operator on regions in the plane and space, Eigenvalue problems on the disc and ball - spherical harmonics. Lecture (3 hr)  
Sturm-Liouville operator on a bounded interval and eigenvalue problems. Tutorial (1 hr)  
Week 11 Application: Solving initial boundary-value problems for parabolic and hyperbolic equations. Lecture (3 hr)  
The Schrödinger operator on regions in the plane and space, Eigenvalue problems on the disc and ball - spherical harmonics. Tutorial (1 hr)  
Week 12 Poisson's equation and Green's function Lecture (3 hr)  
Application: Solving initial boundary-value problems for parabolic and hyperbolic equations. Tutorial (1 hr)  

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. employ foundational techniques to analyze and solve a range of different types of partial differential equations
  • LO2. explain how classical PDEs are derived and their application in different types of problems
  • LO3. solve classical 2nd-order linear differential equations and apply appropriate boundary and initial conditions
  • LO4. apply the theory of orthogonal polynomials to solve a range of structured PDEs
  • LO5. calculate solutions or perform analysis of classical nonlinear PDEs
  • LO6. synthesise solution methods and equations analysis to classify solutions of nonlinear PDEs
  • LO7. communicate mathematical analysis accurately, completely and correctly using algebraic, computational, or graphical methods

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

This year we swapped the theory of first-order and 2nd-order equations.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.