MT5846 Advanced Computational Techniques

Academic year

2024 to 2025 Semester 2

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Planned timetable

12 noon Monday (even weeks), Tuesday, Thursday.

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Dr I Kyza Athanasouli

Dr I Kyza Athanasouli
This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

This module introduces students to some of the ideas, techniques and constraints that underpin modern approaches to the numerical modelling of physical processes that may be described by partial differential equations. Students will gain experience in implementing a variety of standard numerical methods where they will carry out project work involving code development, testing and analysis/interpretation of results.

Relationship to other modules

Pre-requisites

BEFORE TAKING THIS MODULE YOU MUST PASS MT4112

Assessment pattern

Coursework = 100%

Re-assessment

Oral examination = 100%

Learning and teaching methods and delivery

Weekly contact

2.5 lectures (x 10 weeks), 1 practical (x 11 weeks)

Scheduled learning hours

27

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

122

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • Be familiar with standard numerical methods for solving partial differential equations
  • Have an understanding of how some basic PDEs can be applied to real world problems
  • Be confident in coding the introduced PDEs using various numerical methods
  • Demonstrate data visualisation skills using appropriate software
  • Be able to write/present scientific reports in a well structured and readable form