An important component of the Monash Passport II program are depth units. In 2010 a call for EOIs for the development of depth units was issued to all faculties. The FIT proposal for the development of an introductory inter-disciplinary unit on computational science and computational modelling was approved and funded. This unit is the outcome of this development. This is a strategic opportunity for FIT to demonstrate its relevance to other disciplines and build interest for computing in other disciplines.

alignment with approval requirements of UPC 1/2012

As a passport depth unit, this unit is not specifically targeted at FIT students, but rather at all students in the university, specifically those interested in science. It is thus independent of FIT's degree programs. However, it is planned to also make this unit available as an introductory unit to students in the computational science specializations of the BCS, BSE, and BSci.

upon successful completion of the unit, students will have

* awareness of

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

• 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 visualization and of data mining techniques for data analysis
• the role of high performance computation in computational science

• 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)

• conduct basic computational experiment in at least one chosen application domain (with tools that do not require programming)
• be able to perform basic computational data interpretation with visual and non-visual methods
• be able to critically evaluate such experiments

Upon successful completion of the unit students will have the ability to 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.

020100

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. Visualization 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.

The proposed 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 modeling; is computational science a paradigm shift?; data-intensive research; virtual collaboration; the scope of e-Research.

none. All prescribed reading material will be provided online.

Recommended reading for advanced students: Introduction to Computational Science: Modeling and Simulation for the Sciences Angela B. Shiflet & George W. Shiflet

on campus with additional on-line materials

This unit will be team taught, i.e. selected lectures will be given by specialists in the particular area. The teaching arrangements may include out-of-classroom teaching and excursions to research facilities.

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

Hurdle requirement: active participation in Pracs and Lectures at least 50% of weeks = 6/12 marks a mark can be each week earned by active contributions, including informed questions in lectures and pracs participation in class discussions contributions to online forums contribution of materials to online collections

Minimum total expected workload equals 12 hours per week comprising:

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

(b.) Additional requirements (all students):

Laboratories must follow tutorials

FIT

Faculty of Science

none