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Unit outline_

MATH3078: PDEs and Waves

Semester 2, 2024 [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
? 
6cp from (MATH2X21 or MATH2X65 or MATH2067) and 6cp from (MATH2X22 or MATH2X61)
Corequisites
? 
None
Prohibitions
? 
MATH3978 or MATH4078
Assumed knowledge
? 

[MATH2X61 and MATH2X65] or [MATH2X21 and MATH2X22]

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Robert Marangell, robert.marangell@sydney.edu.au
Lecturer(s) Florica-Corina Cirstea, florica.cirstea@sydney.edu.au
Robert Marangell, robert.marangell@sydney.edu.au
The census date for this unit availability is 2 September 2024
Type Description Weight Due Length
Supervised exam
? 
Final exam
Written exam, including computational and proof-based questions.
60% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Small test Quiz 1
Written calculations and arguments
15% Week 05
Due date: 29 Aug 2024 at 15:45

Closing date: 29 Aug 2024
45 minutes
Outcomes assessed: LO1 LO7 LO5 LO2
Short release assignment Assignment
Written assignment
10% Week 09
Due date: 29 Sep 2024 at 23:59

Closing date: 29 Sep 2024
10 days
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Small test Quiz 2
Written calculations and arguments
15% Week 12
Due date: 24 Oct 2024 at 15:45

Closing date: 24 Oct 2024
45 minutes
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2

Assessment summary

  • Quizzes: Two quizzes will be held in class. Each quiz is 45 minutes. 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.
  • Assignment: one assignment to provide written solutions to questions. The assignment must be submitted electronically, as one single typeset file only, via Canvas by the deadline. Note that your assignment will not be marked if it is not typset or is submitted sideways or upside down. It is your responsibility to check that your assignment has been submitted correctly and that it is complete (check that you can view each page). Late submisions will not be accepted.

  • Final exam: If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator.

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 sydney.edu.au/students/guide-to-grades.

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.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Week 01 Introduction to PDEs - What is a PDE? Classification of PDEs; Examples of PDEs. Well-posed problems. Harmonic functions. Lecture (3 hr) LO1 LO2 LO4 LO5
Week 02 PDEs and boundary conditions. Initial conditions for evolution equations. Linear PDEs. The principle of superposition for linear and homogeneous equations. Nonlinear PDEs: semilinear; quasilinear and fully nonlinear. Lecture (3 hr) LO1 LO4 LO5
Introduction to PDEs - What is a PDE? Classification of PDEs: stationary or evolution; linear or non-linear; homogeneous or non-homogeneous. Harmonic functions. Properties of the Laplace operator and the fundamental solution of the Laplace equation. Tutorial (1 hr) LO1 LO4 LO5
Week 03 1st-order linear PDEs and the method of characteristics. Lecture (3 hr) LO2 LO4 LO5
Check whether a PDE is linear, semilinear, quasilinear, fully nonlinear and find its order. Solve simple PDEs. Well-posed/Ill-posed problems. The method of characteristics. Tutorial (1 hr) LO1 LO5
Week 04 The method of characteristics for nonlinear PDEs. The wave equation and d'Alembert's Formula. Classification of 2nd order linear PDEs in two variables: hyperbolic, parabolic and elliptic types. Lecture (3 hr) LO2 LO5
1st-order linear PDEs and the method of characteristics for solving initial value problems. Tutorial (1 hr) LO2 LO4 LO5
Week 05 Linear second-order PDEs with constant coefficients in two variables: canonical form depending on the type (hyperbolic, parabolic, elliptic). Introduction to Fourier series and sufficient conditions for convergence. Lecture (3 hr) LO2 LO4 LO5
Initial boundary value problems for the heat equation: uniqueness of solutions using the energy method. Green's formulas. The Dirichlet principle for Poisson's equation. Tutorial (1 hr) LO2 LO4 LO5
Week 06 Fourier series and the Fourier method of separation of variables. Eigenvalue problems. Self-adjoint operators and positive-definite (positive semi-definite) operators. Lecture (3 hr) LO2 LO4 LO5
Determine whether a linear second-order PDE in two variables is hyperbolic, parabolic or elliptic. The homogeneous wave equation and d'Alembert's formula. Tutorial (1 hr) LO2 LO4 LO5
Week 07 Properties of eigenvalues for self-adjoint and positive-definite (positive semi-definite) operators. The adjoint of a linear operator, properties and examples. Minimisation principle. Sturm-Liouville boundary value problems. Lecture (3 hr) LO2 LO4 LO5
Determine the type of a second order linear PDE in two variables and bring it to the canonical form using a suitable change of variables. Eigenvalue problems. The Fourier method of separation of variables to solve various PDEs. Tutorial (1 hr) LO2 LO4 LO5
Week 08 The regular Sturm-Liouville eigenvalue problem and properties of the eigenvalues. Non-homogeneous equations in the context of Sturm-Liouville theory. Lecture (3 hr) LO2 LO4 LO5
Sturm-Liouville eigenvalue problems. Solving boundary value problems using generalised Fourier series. Tutorial (1 hr) LO2 LO4 LO5
Week 09 The Laplace equation in circular domains. The eigenvalue problem for the Laplacian: The Dirichlet problem in a disk. Lecture (3 hr) LO2 LO5
The Helmholtz equation, subject to a homogeneous Dirichlet boundary condition, and the method of separation of variables to determine its eigenvalues and eigenfunctions. Solve the Dirichlet boundary value problems for the Laplace equation in a disk or in a sector. The method of separation of variables to solve non-homogeneous boundary value problems. Tutorial (1 hr) LO2 LO5
Week 10 Fourier-Bessel expansion and application to the vibration of a circular membrane. The eigenvalue problem for the Laplacian in a ball in R^3. The heat equation: scale invariant solutions and the fundamental solution in dimension 1. Lecture (3 hr) LO2 LO4 LO5
Laplace's equation in a cylinder. The method of separation of variables for elliptic, parabolic and hyperbolic type problems. Tutorial (1 hr) LO2 LO5
Week 11 Heat equation: Fundamental solution and properties; solution to initial-value problem. Elliptic problems: Laplace's equation and Poisson's equation. Lecture (3 hr) LO2 LO4 LO5 LO6
One-dimensional heat equation: self-similar solutions; the fundamental solution. The Cauchy problem for reaction-diffusion equations. Regular Sturm-Liouville eigenvalue problems and non-homogeneous initial boundary value problems. Tutorial (1 hr) LO2 LO4 LO5
Week 12 Poisson's equation; Representation formula using Green's function. Lecture (3 hr) LO2 LO3 LO4 LO5
Green's function in dimension 1; Sturm-Liouville eigenvalue problems; Solving initial boundary-value problems. Tutorial (1 hr) LO2 LO4 LO5 LO6
Week 13 Properties of harmonic functions: mean value property, strong maximum principle; Harnack's inequality; Revision. Lecture (3 hr) LO2 LO3 LO4 LO5
Green's function and Poisson's formula. Tutorial (1 hr) LO2 LO3 LO4 LO5

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. demonstrating the ability to recognize different types of partial differential equations: "linear" or "nonlinear", "order of the given equation", "homogeneous" or "inhomogeneous", and if it concerns 2nd-order equations, whether they are of "elliptic", "parabolic", or "hyperbolic" type
  • LO2. demonstrating the conceptional understanding of how to apply different methods for solving different types of partial differential equations. Those methods include the use of classical ODE-concepts to solve PDEs
  • LO3. understanding the definitions, main theorem, and corollaries of Green's functions and Poisson kernel
  • LO4. be fluent with "change of variable" into polar, cylindrical and spherically coordinates and to be able to compute partial derivatives in these coordinates
  • LO5. develop an appreciation and strong working knowledge of the theory and application of elementary partial differential equations
  • LO6. be fluent in using generalized Fourier transforms to solve parabolic and hyperbolic initial boundary value problems where the spatial variable might be of more than one variable
  • LO7. Communicate mathematical analysis accurately, completely, and correctly using algebraic, computational, or graphical methods, in a typeset manner when appropriate.

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.

There have been changes regarding the order in which the topics have been introduced. More details and exercises have been added.

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.