MON1002 The changing face of science: how computational science helps us make sense of the world - Semester 1, 2013

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.

Mode of Delivery

Contact Hours

2 hrs lecture/wk, 3 hrs laboratory/wk

Workload requirements

Students are expected to spend a total of 12 hours per week on average during semester on this unit.

This includes 2 hours of lecture and 3 hours of laboratory work per week.

The remaining 7 hours are allocated to preparation and revision, reading time, and solving assignments.

Chief Examiner

Campus Lecturer

Tutors

Academic Overview

Learning Outcomes

• 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 visualisation 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);
• 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.

Unit Schedule

*Unit Schedule details will be maintained and communicated to you via your learning system.

Assessment Summary

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

Teaching Approach

• Lecture and tutorials or problem classes
  The unit is taught on campus with additional on-line materials. It will be team taught, i.e. selected lectures will be given by specialists in a particular area. 
• Problem-based learning
  The unit employs an active learning approach with components of peer instruction. In practice this means that the lectures will not only be used to present new information, but much of the classroom time will be dedicated to the discussion of example problems and to group work. Laboratories are a direct extension of this but focus more strongly on the individual contribution of the students. They will be used to solve complex problems with the guidance of a tutor.
  
  The boundaries between lecture and laboratory are thus somewhat fluid.

Assessment Requirements

Assessment Policy

Faculty Policy - Unit Assessment Hurdles (http://www.infotech.monash.edu.au/resources/staff/edgov/policies/assessment-examinations/unit-assessment-hurdles.html)

Academic Integrity - Please see the Demystifying Citing and Referencing tutorial at http://lib.monash.edu/tutorials/citing/

Assessment Tasks

Hurdle Requirements

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

Participation

• Assessment task 1

  Title:

  Modelling Project Part 1: Model Building and Evaluation

  Description:

  Part 1 of the modelling project consists of the initial description of a modeling project in the student's chosen application domain. Deliverables include 1) the description of the phenomenon to be investigated, 2) specification of the hypothesis that will be investigated with the computational experiment, 3) choice of the modelling approach and reasons for this choice, 4) experimental plan, 5) detailed specification of the model.

  Weighting:

  30% (of non-exam component)

  Criteria for assessment:

  Marks will be allocated according to correctness, completeness, and depth of the analysis (1-3), feasibility and adequateness of the experimental plan (4), and completeness, correctness, and style of the model (5).

  Particular importance will be placed on how well underlying principles and theories are demonstrated in the student's answer.

  Due date:

  26 April 2013

  Remarks:

  Topics for the modelling project must be individually approved by the chief examiner.

• Assessment task 2

  Title:

  Modelling Project Part 2: Implementation and Use

  Description:

  Part 2 of the Modelling Project consists of the implementation of the model proposed in Part 1 and its use to conduct the experimental plan. The deliverables are the model implementation, a brief evaluation of the model's affordances and shortcomings, and an evaluation of the experimental results.

  Weighting:

  30% (of non-exam component)

  Criteria for assessment:

  Marks will be allocated according to correctness and style of the implementation, scope and correctness of the experimental evaluation, and depth of insight into the model's characteristics.

  Due date:

  15 May 2013

  Remarks:

  Any aspect of the project may be modified from the proposal made in Part 1 in response to feedback received. However, all such modifications must be individually approved by the chief examiner.

• Assessment task 3

  Title:

  Essay

  Description:

  This is an individually written essay on the application of computational models in an application domain that students can choose according to their individual interests. 

  Weighting:

  25% (of non-exam component)

  Criteria for assessment:

  Marks will be allocated according to how well the examples are chosen to demonstrate the extension of conventional approaches to the problem by computational methods, the quality of the student's argument, and the adequateness of the report style.

  Due date:

  10 May 2013

• Assessment task 4

  Title:

  Peer Assessment

  Description:

  An important aspect of the unit is that students will learn how the same computational approaches apply to very different application areas and disciplines. This will be facilitated through cross-discipline peer evaluation. Groups will be formed according to chosen application areas and modelling approaches and apply the same approaches to problems in their respective disciplines. Subsequently students will perform a cross-evaluation in which they critically evaluate the work of another student or group of students from a different discipline area.

  Weighting:

  15% (of non-exam component)

  Criteria for assessment:

  Marks will be allocated according to the depth of demonstrated insight into the modelling approach, correctness of the evaluation, the quality of the student's argument, how well underlying principles and theories are demonstrated in the student's answer, and the adequateness of the report style.

  Due date:

  31 May 2013

Examinations

• Examination 1

  Weighting:

  60%

  Length:

  3 hours

  Type (open/closed book):

  Closed book

  Electronic devices allowed in the exam:

  None

Learning resources

Feedback to you

• Informal feedback on progress in labs/tutes
• Graded assignments with comments

Extensions and penalties

Returning assignments

Resubmission of assignments

Students will not be allowed to resubmit assessment unless special consideration applies.

Referencing requirements

Please refer to the Library Guides for Citing and Referencing at http://guides.lib.monash.edu/content.php?pid=88267&sid=656564

Assignment submission

Online submission

Recommended text(s)

A.B. Shiflet and G.W. Shiflet. (2006). Introduction to Computational Science. () Princeton University Press.

Aidan Lane, Bernd Meyer and Jonathan Mullins. (2012). Simulation with Cellular. (available as iBook or interactive PDF at: http://monash-blockbooks.appspot.com) .

Examination material or equipment

The exam is a closed book exam.

If computational equipment is required it will be provided by the University.

You must not use any of your own computating equipment during the exam.

Other Information

Policies

Graduate Attributes Policy

Student services

Monash University Library

Disability Liaison Unit

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This is the first time MON1002 is being offered.