Undergraduate - UnitFIT3139 - Computational science

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Synopsis

The unit provides an overview of computational science and an introduction to the central methods in this field. While it is not tied to any particular field of scientific study, it requires a general scientific background at advanced introductory level.

Topics include: the role of computational tools and methods in 21st century science; modelling and simulation; continuous vs discrete models; analytic versus numeric models; deterministic versus stochastic models; Monte-Carlo methods; epistemology of simulations; visualisation; high-dimensional data analysis; optimisation; limitations of numerical methods; high-performance computing and data-intensive research.

Each topic area will be introduced with a general overview followed by a discussion of one or a few selected methods in full technical detail. These will be practiced in tutorials and laboratories, which will also acquaint the students with standard software packages for scientific computing (for example, Mathematica, Matlab, Maple, Sage).

Seminars and guest lectures will present case studies and link to current topics in research.

Applications examples will be drawn from Physics, Biology, Bioinformatics, Chemistry, Social Science, etc.

Outcomes

Upon successful completion of the unit students will -

• understand the role of computational tools and methods in modern science;
• understand the process of model construction, model fitting, model verification and analysis in scientific problem solving;
• understand the differences between the core modelling approaches (numeric versus analytic; continuous versus discrete; linear versus non-linear; deterministic versus stochastic);
• understand the implications of choosing a particular modelling approach;
• understand central computational methods for the analysis of models in each of these classes
• understand the role of simulation and visualisation;
• be introduced to at least one standard scientific software package for model construction and analysis;
• have an general overview of high-performance techniques in scientific computing and of methods for data-intensive research (storage, archiving etc).

Assessment

Examination (3 hours): 50%, In-semester assessment: 50%

Chief examiner(s)

Dr Arun Konagurthu

Contact hours

2 hrs lectures/wk, 3 hrs lab/wk, 1 hr tutorial alternating fortnightly with attendance at Monash University Research Projects Abroad (MURPA) seminars.

Prerequisites

One of MAT1841, MAT2003, ENG1091, MTH1030, MTH1035 or equivalent plus any introductory programming unit (eg FIT1002, ECE2071, TRC2400, or equivalent)

Additional information on this unit is available from the faculty at:

http://www.infotech.monash.edu/units/fit3139/