units

MON1002

Faculty of Information Technology

Undergraduate - Unit

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6 points, SCA Band 2, 0.125 EFTSL

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered, or view unit timetables.

LevelUndergraduate
FacultyFaculty of Information Technology
Monash Passport categoryDepth (Enhance Program)
OfferedNot offered in 2014

Quota applies

This unit can be taken by a maximum of 25 students (due to use of specialised facilities and method of teaching). Selection is on a first-in, first enrolled basis.

Synopsis

Research has experienced profound methodological changes in the last decades. A significant part of scientific enquiry now relies on computational approaches to complement theory and experiment. This a fundamental shift. In the words of Nobel laureate Ken Wilson: computation has become the "third leg" of science. Simulations allow us to perform virtual experiments that are too dangerous, too costly, unethical, or plainly impossible to conduct in reality. Visualisation offers us entirely new ways to explore and understand data, and only computational analysis makes it possible to cope with the vast amounts of data that contemporary science and engineering must process.

Computational science and eResearch are core drivers of innovation. Bioinformatics, climate studies, and ecological modelling are among the most prominent and most important examples, but the fundamental impact of this shift is felt far beyond the so-called "hard" sciences.

Arguably, one of the pivotal influences of computational science is to change the character of whole disciplines by making it possible for them to perform "hard" qualitative data-based studies in areas where this was impossible before. For example, social science researchers can conduct quantitative studies by simulating virtual societies in order to understand the ramifications of hypothetical changes in behaviour or policies. Medical researchers can simulate the spread of world-wide epidemics to evaluate possible containment methods, and economists can use simulations to "measure" the impact of such epidemics and other disasters on national and global financial systems.

This unit will equip students with a thorough understanding of how computational science relates to and extends traditional methods. Students will have the opportunity to work on problems from their "home discipline" which will enable them to understand the potential and limitations of computational studies in these fields.

Topics include: history of science; the role of computational methods; simulations and virtual experiments; capturing complex systems; the limits of modelling; is computational science a paradigm shift?; data-intensive research; virtual collaboration; the scope of e-Research.

Outcomes

On successful completion of this unit, students will have:

An awareness of:

  • the potential of computational and mathematical modelling in experimental work;
  • the fundamental limitations of computational experiments.

An understanding of:

  • the role of hypothesis, experiment, model, and theory in the classical scientific approach;
  • the expectation-observation-reflection refinement cycle in experimental research;
  • the role of a mathematical model;
  • the difference between mathematical and computational models;
  • the role of simulation in modern science;
  • the role of visualisation and of data mining techniques for data analysis;
  • the role of high performance computation in computational science.

Knowledge of:

  • different types of model (mental, computational, mathematical, animal, ...);
  • the basic categories of computational modelling: analytic versus simulation; ab initio versus coarse-grained; discrete versus continuous; deterministic versus stochastic;
  • inherent difficulties of computational approaches (e.g. parameter sensitivity and combinatorial explosions);

The ability to:

  • conduct basic computational experiment in at least one chosen application domain (with tools that do not require programming);
  • perform basic computational data interpretation with visual and non-visual methods;
  • critically evaluate such experiments;
  • work in teams to design, conduct, evaluate, review, and critique experiments that address basic research questions in their chosen application domain and to explain the designs and results to outsiders.

Assessment

Examination (3 hours): 60%; In-semester assessment: 40%

Chief examiner(s)

Workload requirements

Minimum total expected workload equals 12 hours per week comprising:

(a.) Contact hours for on-campus students:

  • Two hours of lectures
  • One 3-hour laboratory

(b.) Additional requirements (all students):

  • A minimum of 7 hours independent study per week for completing lab and project work, private study and revision.

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