Most natural and man-made systems are highly complex and so are beyond reasonable efforts to investigate their behaviours analytically. Instead computational approaches are needed. In this module you will explore a variety of approaches to computational modelling and simulation. Throughout the module, ideas and tools will be developed for real-world relevant applications.

In this module you will develop expertise in modern mathematical and computational modelling tools used to simulate and so understand the behaviour of complex systems arising in ecology, environmental science and energy engineering. You will explore modelling from first principles and implement them computationally using MATLAB, Python, R and/or similar high-level language. The module follows a two-step learning process, where, (i) you are introduced to a modelling approach, and then, (ii) you develop the approach within a substantive application. With this two-step approach you will cover a number of different techniques relevant to the MSci pathway.

On successful completion of this module, you should be able to:

Module Specific Skills and Knowledge:

1 Formulate mathematical models from first principles;

2 Understand the underlying principles of the modelling approach;

3 Implement models in MATLAB, Python, R and/or similar high-level language;

4 Use models to understand qualitative behaviour of systems;

Discipline Specific Skills and Knowledge:

5 Decide on appropriate modelling approaches;

6 Use a range of computational platforms/software;

Personal and Key Transferable/Employment Skills and Knowledge:

7 Communicate the value of modelling and simulation to a range of end users in ecology, environmental science or energy engineering.

The aim is to make sure the approaches are modern and current and so the specific modelling approaches may vary over time. Each modelling approach will be covered in three-week blocks in which the modelling approach is introduced and then applied in mini-project based work. Three approaches will be covered. Potential modelling approaches may include:

Approximate Bayesian Computation (ABC);

Agent-based Modelling;

Complexity;

Computational Fluid Dynamics;

Finite element/finite difference techniques;

Optimisation;

Simulation-based Markov Chain Monte Carlo (MCMC);

Simulink.

If a module is normally assessed entirely by coursework, all referred/deferred assessments will normally be by assignment.

If a module is normally assessed by examination or examination plus coursework, referred and deferred assessment will normally be by examination. For referrals, only the examination will count, a mark of 40% being awarded if the examination is passed. For deferrals, candidates will be awarded the higher of the deferred examination mark or the deferred examination mark combined with the original coursework mark.

Basic Reading:

ELE: http://vle.exeter.ac.uk

Reading list for this module: