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Mathematical modelling is important for understanding and engineering many facets of complex systems. The aim of this subject is for students to understand the range and use of mathematical theories and notations in the analysis of discrete systems, how to abstract the key aspects of a problem into a model to handle complexity, and how models can be employed to verify large-scale complex software systems.
Topics covered will be selected from: Deterministic and stochastic modelling; dynamical systems; cellular automata; agent-based modelling; complex networks; simulation and analysis of complex systems; concurrent systems modelling, analysis and implementation; process algebra; temporal logic and model checking.
Intended learning outcomes
INTENDED LEARNING OUTCOMES (ILO)
On completion of this subject the student is expected to:
- Identify and abstract the key features of a range of complex system
- Understand the theoretical basis underpinning the analysis of complex systems
- Analyse models of discrete and concurrent systems using a range of modern techniques
- Evaluate and select, amongst different modelling techniques, the most appropriate for analysing specific systems
- Create mathematical/computational models to analyse and verify the behaviour of complex systems.
On completion of this subject, students should have the following skills.
• Ability to undertake problem identification, formulation and solution
• Ability to utilise a systems approach to analysing software properties
• Capacity for independent critical analysis of models, and self-directed research for mathematical modelling approaches
• Intellectual curiosity and creativity, including understanding of the philosophical and methodological ideas behind research in software systems analysis
• Openness to new ideas and unconventional critiques of received wisdom
Last updated: 2 December 2019