MTH4321 - Methods of computational mathematics - 2019

6 points, SCA Band 2, 0.125 EFTSL

Undergraduate, Postgraduate - Unit

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.

Faculty

Science

Organisational Unit

School of Mathematical Sciences

Chief examiner(s)

Dr Tiangang Cui

Coordinator(s)

Dr Tiangang Cui

Unit guides

Offered

Clayton

  • First semester 2019 (On-campus)

Prerequisites

Enrolment in the Master of Mathematics

Prohibitions

MTH5321

Synopsis

Computational methods are of paramount importance for solving real-world problems in applied mathematics.This unit teaches widely used numerical methods for problems from science, engineering, biology and finance that are modeled by partial differential equations (PDEs). The unit covers numerical methods for PDEs of elliptic, parabolic and hyperbolic type, as well as advanced solution methods for the linear and nonlinear systems of equations that may arise from the discretisation of the PDEs. Topics covered may include finite difference methods, finite element methods, and finite volume methods; iterative and multigrid solvers for linear and nonlinear systems; nonlinear hyperbolic conservation laws; and other topics in numerical PDEs.The concepts of numerical accuracy, stability and efficiency play a central role in the unit. Students will receive an introduction to the theory of the numerical methods (with derivations of the methods and some proofs), and will learn to implement the computational methods efficiently. Applications will be covered from various domains such as heat transfer, option pricing, biology, and fluid mechanics.

Outcomes

On completion of this unit students will be able to:

  1. Explain the mathematical theory behind a selection of important numerical methods for PDEs, including the derivation of the methods and the analysis of their properties.
  2. Explain and apply notions of accuracy, stability and computational cost when solving PDE problems numerically.
  3. Demonstrate proficiency in numerical methods for PDEs and linear system solving, and apply them to problems in science, engineering, biology and finance.
  4. Implement advanced numerical PDE methods, and demonstrate the correctness and efficiency of the implementations in systematic computational tests.
  5. Apply critical thinking and demonstrate written and oral communication skills in the field of computational mathematics.

Assessment

Examination (3 hours): 60% (Hurdle)

Continuous assessment: 40%

Hurdle requirement: To pass this unit a student must achieve at least 50% overall and at least 40% for the end-of-semester exam.

Workload requirements

3 hours of lectures and 1 hour of tutorial per week

See also Unit timetable information

This unit applies to the following area(s) of study

Master of Mathematics