This unit provides an overview of computational science and an introduction to its central methods. It covers the role of computational tools and methods in 21st century science, emphasising modelling and simulation. It introduces a variety of models, providing contrasting studies on: continuous versus discrete models; analytical versus numerical models; deterministic versus stochastic models; and static versus dynamic models. Other topics include: Monte-Carlo methods; epistemology of simulations; visualisation; high-dimensional data analysis; optimisation; limitations of numerical methods; high-performance computing and data-intensive research. A general overview is provided for each main topic, followed by a detailed technical exploration of one or a few methods selected from the area. These are applied workshops which also acquaint you with standard scientific computing software (e.g., Mathematica, Matlab, Maple, Sage). Applications are drawn from disciplines including Physics, Biology, Bioinformatics, Chemistry, Social Science.
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.
Rationalise the role of simulation and data visualisation in science;
Explain and apply the process of computational scientific model building, verification and interpretation;
Evaluate the implications of choosing different modelling approaches;
Analyse the differences between core classes of modelling approaches (Numerical versus Analytical; Linear versus Non-linear; Continuous versus Discrete; Deterministic versus Stochastic);
Apply all of the above to solving idealisations of real-world problems across various scientific disciplines.
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