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MTH3340 · Numerical methods for partial differential equations

Official Handbook

2026 Handbook6 credit pointsLevel 3School of Mathematics

Last checked: 23 Aug 2026 UTC

Overview

Partial differential equations are ubiquitous in many domains of sciences and industry, as they model phenomena with spatial and temporal variations. Most of these models are too complex to be exactly solved, and numerical methods are the only way to gather quantitative behaviour on the solutions. This unit covers the design, analysis and implementation of numerical methods for partial differential equations. Topics covered can include finite difference methods, finite element methods, finite volume methods, error analysis, elliptic equations, parabolic equations, implementation in dynamic languages (such as Python or Julia). The focus will be on the design of the methods, their mathematical analysis, and their implementation and numerical testing.

Areas of study: Applied mathematics Pure mathematics Mathematics Mathematical statistics

Offerings

CampusTeaching periodMode
ClaytonSecond semesterTeaching activities are on-campus (ON-CAMPUS)

Assessment

The Handbook lists an examination for this unit.

#AssessmentTypeWeightHurdle
1Continuous assessmentDemonstration50%
2Final assessment - Exam (3 hours and 10 minutes)Examination50%

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

Learning outcomes

  1. Demonstrate understanding of the need for numerical methods to gather quantitative information on the solutions to partial differential equations.
  2. Design and analyse the convergence and stability of numerical methods for a range of partial differential equations.
  3. Select appropriate discretisation techniques based on their particular features, and the features of the considered model.
  4. Implement specific numerical methods in a high-level language (such as Python or Julia), and interpret numerical outputs.
  5. Demonstrate advanced skills in the written and oral presentation of theoretical and practical numerical problems involving partial differential equations.

Workload

• Three 1-hour seminars; • One 2-hour applied class (in weeks 2-12) and • 7 hours of independent study per week

ActivityDuration
Applied sessions22 hours
Seminars36 hours

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