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Computational Differential Equations (MAST90026)

Graduate courseworkPoints: 12.5Not available in 2019

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Year of offerNot available in 2019
Subject levelGraduate coursework
Subject codeMAST90026
FeesSubject EFTSL, Level, Discipline & Census Date

Many processes in the natural sciences, engineering and finance are described mathematically using ordinary or partial differential equations. Only the simplest or those with special structure can be solved exactly. This subject discusses common techniques for computing numerical solutions to differential equations and introduces the major themes of accuracy, stability and efficiency. Understanding these basic properties of scientific computing algorithms should prevent the unwary from using software packages inappropriately or uncritically, and provide a foundation for devising methods for nonstandard problems. We cover both time-independent problems, in one and higher space dimensions, and evolution equations of hyperbolic or parabolic type.

Intended learning outcomes

After completing this subject, students should:

  • appreciate how and why numerical methods are developed to solve differential equations commonly arising in finance, science and engineering;
  • understand the chief factors to be considered in choosing an appropriate algorithm for a given class of problem;
  • acquire high level numerical tools and knowledge that can be used to solve a range of problems in science and engineering;
  • gain the ability to pursue further studies in this and related areas.

Generic skills

In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include:

  • problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
  • analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
  • collaborative skills: the ability to work in a team;
  • time-management skills: the ability to meet regular deadlines while balancing competing commitments.

Last updated: 11 November 2018