LEARNING OUTCOMES (By the end of the module the student should be able to....)

Teaching and learning methods enabling students to achieve this learning outcome.

Assessment methods will measuring the achievement of this learning outcome.

(a) Subject knowledge and understanding

Appreciate how inference underpins the modelling of multivariate complex systems in terms of fewer state variables.
Understand how fluctuations depend on system size, and limit of large numbers .
Ability to model emergent order in equilibrium systems.
Experience modelling complex systems whose microscopic dynamics is less clear.
Construct transport models.
Experience modelling emergent behaviour in non-equilibrium systems.

Lectures (basic ideas, results, worked examples);
Guided reading in textbooks and selected journal papers;
Computer workshops on modelling physical systems as well as ones outside physics.

Oral examination;
Written reports;

(b) Key Skills

Teamwork
Writing compiled code for numerical simulations.

Computer workshops will help students to develop their computational /numerical skills from package-based computing (eg. Matlab) to compiled code (eg. C)

Observation of workshop activities;
written reports.

(c) Cognitive Skills

Judgement of numerical data
Understanding how the tools of statistical mechanics can be used in non-conventional applications.

Classwork

Written reports, oral examination.

(d) Subject-Specific/Professional Skills