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莫纳什大学MTH3340

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 … For more content click the Read more button below.
Offerings
S2-01-CLAYTON-ON-CAMPUS
Location:Clayton
Teaching period:Second semester
Attendance mode:On-campus
Rules
Enrolment Rule
PREREQUISITES:  MTH2032 and (MTH2051 or MTH3051), or MTH2040, or ENG2005

Contacts

Chief Examiner(s)
Professor Santiago Badia
Offering(s):
  • Applies to all offerings
Unit Coordinator(s)
Professor Santiago Badia
Offering(s):
  • Applies to all offerings
Learning outcomes
On successful completion of this unit, you should be able to:
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.