The use of mathematical models has grown rapidly in recent years, owing to the advent of cheap and powerful computers, expanding from applications in the physical and earth sciences to the biological and environmental sciences, and now into industry and commerce.

In this course we study:

• The process of starting with an initial succinct non-mathematical description of a problem
  Formulate associated mathematical models.
• Introduce new mathematical techniques and then determine and interpret solutions that are useful in a real life context.
• General computational and mathematical techniques and strategies will be introduced by examining specific scientific and industrial problems.

The topics to be covered in this course include:

• Model type selection and formulation
• Data analysis techniques (time/space and frequency domain)
• State Space and Transfer Function Models
• Model Structure Identification
• Testing and Sensitivity Analysis

Computations will be done using modern high level scientific computing environments such as SCILAB or PYTHON. 

Note: Graduate students attend joint classes with undergraduates but are assessed separately.

On satisfying the requirements of this course, students will have the knowledge and skills to:

Assessment will be based on:

• Exam (40%; LO 1, 2, 3, 4, 5)
• Project designing and evaluating a model for a selected system (30%; LO 1, 2)
• Three assignments demonstrating ability to apply techniques (10% each; LO 2, 3, 5)

Bachelor degree; with second year Maths.

Requirtes undergraduate degree with suitable second year Maths (or equivalent)