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Unit outline_

COSC2902: Computational Modelling (Advanced)

Semester 1, 2023 [Normal day] - Camperdown/Darlington, Sydney

This unit will introduce a wide range of modelling and simulation techniques for tackling real-world problems using a computer. Data is often expensive to obtain, so by harnessing the enormous computational processing power now available to us we can answer what if questions based on data we already have. You will learn how to break a problem down into its key components, identifying necessary assumptions for the purposes of simulation. You will learn how to develop suitable metrics within computational models, to allow comparison of simulation data with real-world data. You will learn how to iteratively improve simulations as you validate them against real results, and you will gain experience in identifying the types of exploratory questions that computational modelling opens up. Programming will be in python. You will learn how to generate probabilistic data, solve systems of differential equations numerically, and tackle complex adaptive systems using agent-based models. Dynamical systems ranging from traffic flow to social segregation will be considered. By doing this unit you will develop the skills to go behind your data, understand why the data you observe might be as it is, and test scenarios which might otherwise be inaccessible. This is an advanced unit. It runs jointly with the associated mainstream unit, however the lab work and assessment requires a greater level of academic rigour. You will be required to engage in more challenging real-world computational modelling problems than the mainstream unit, and explore more deeply the reasons behind simulation results.

Unit details and rules

Academic unit Physics Academic Operations
Credit points 6
Prerequisites
? 
48 credit points of 1000 level units with an average of 65
Corequisites
? 
None
Prohibitions
? 
COSC1003 or COSC1903 or COSC2002
Assumed knowledge
? 

HSC Mathematics; DATA1002, or equivalent programming experience, ideally in Python

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Tristram Alexander, tristram.alexander@sydney.edu.au
Lecturer(s) Carla Verdi, carla.verdi@sydney.edu.au
Tristram Alexander, tristram.alexander@sydney.edu.au
Type Description Weight Due Length
Supervised exam
? 
hurdle task
Final exam
Final exam
40% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Tutorial quiz Class test 1
In-lab practical test
20% Week 06 90 minutes
Outcomes assessed: LO1 LO5 LO4 LO3 LO2
Tutorial quiz Class test 2
In-lab practical test
20% Week 12 90 minutes
Outcomes assessed: LO1 LO5 LO4 LO3 LO2
Assignment Assignment
Written report
10% Week 13
Due date: 26 May 2023 at 23:59
Two weeks
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Small continuous assessment Weekly lab submissions
Online submission of two checkpoints per lab
10% Weekly Lab time + 0-2 additional hours per lab
Outcomes assessed: LO1 LO6 LO5 LO4 LO3 LO2
hurdle task = hurdle task ?

Assessment summary

  • Weekly lab submissions: work through a Jupyter notebook and submit two lab checkpoints per lab.  The best 34 checkpoints (out of 40) will count towards the final mark for this course component.
  • Class test 1: work through a Jupyter notebook and submit at conclusion of test
  • Class test 2: work through a Jupyter notebook and submit at conclusion of test
  • Assignment: Conduct appropriate numerical investigations and submit a written report detailing results
  • Exam: Conducted as a proctored Canvas quiz. If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam).  The alternative assessment will meet the same learning outcomes as the original exam.  The format of the alternative assessment will be determined by the unit coordinator.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

At HD level, a student demonstrates a flair for the subject as well as a detailed and comprehensive understanding of the unit material. A ‘High Distinction’ reflects exceptional achievement and is awarded to a student who demonstrates the ability to apply their subject knowledge and understanding to produce original solutions for novel or highly complex problems and/or comprehensive critical discussions of theoretical concepts.

Distinction

75 - 84

At DI level, a student demonstrates an aptitude for the subject and a well-developed understanding of the unit material. A ‘Distinction’ reflects excellent achievement and is awarded to a student who demonstrates an ability to apply their subject knowledge and understanding of the subject to produce good solutions for challenging problems and/or a reasonably well-developed critical analysis of theoretical concepts.

Credit

65 - 74

At CR level, a student demonstrates a good command and knowledge of the unit material. A ‘Credit’ reflects solid achievement and is awarded to a student who has a broad general understanding of the unit material and can solve routine problems and/or identify and superficially discuss theoretical concepts.

Pass

50 - 64

At PS level, a student demonstrates proficiency in the unit material. A ‘Pass’ reflects satisfactory achievement and is awarded to a student who has threshold knowledge.

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Introduction to computational modelling using scientific python packages, including Jupyter notebooks and pandas Lecture and tutorial (2 hr) LO2 LO3
Introduction to computational modelling using scientific python packages, including Jupyter notebooks and pandas Computer laboratory (3 hr) LO2 LO3
Week 02 Introduction to modelling randomness Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4
Introduction to modelling randomness Computer laboratory (3 hr) LO1 LO2 LO3 LO4
Week 03 Simulating exponential and power-law probability distributions Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Simulating exponential and power-law probability distributions Computer laboratory (3 hr) LO1 LO2 LO3 LO4 LO5
Week 04 Discrete time simulations and nonlinear maps Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Discrete time simulations and nonlinear maps Computer laboratory (3 hr) LO1 LO2 LO3 LO4 LO5
Week 05 Multidimensional functions and ordinary differential equations Lecture and tutorial (2 hr) LO1 LO2 LO3
Multidimensional functions and ordinary differential equations Computer laboratory (3 hr) LO1 LO2 LO3
Week 06 Modelling using ordinary differential equations Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Modelling using ordinary differential equations Computer laboratory (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 07 Modelling using differential equations Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Modelling using differential equations Computer laboratory (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 08 Cellular automata Lecture and tutorial (2 hr) LO1 LO2 LO3
Cellular automata Computer laboratory (3 hr) LO1 LO2 LO3
Week 09 Partial differential equations Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Partial differential equations Computer laboratory (3 hr) LO1 LO2 LO3 LO4 LO5
Week 10 Agent-based modelling Lecture and tutorial (2 hr) LO1 LO2 LO3
Agent-based modelling Computer laboratory (3 hr) LO1 LO2 LO3
Week 11 More on agent-based modelling and using classes Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
More on agent-based modelling and using classes Computer laboratory (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 12 Accounting for connectivity: modelling networks Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Accounting for connectivity: modelling networks Computer laboratory (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 13 Review of the computational modelling process Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Review of the computational modelling process Computer laboratory (3 hr) LO1 LO2 LO3 LO4 LO5 LO6

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. given a real-world problem, develop a simplified model
  • LO2. identify and explain assumptions underpinning a model of a real-world problem
  • LO3. obtain a solution to a model using a computer
  • LO4. given a computational model, and simulation output, identify key characteristics of the numerical solution
  • LO5. check validity of simulation output and be able to defend conclusions
  • LO6. develop and use metrics to compare simulation and real-world data.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

General Laboratory Safety Rules

  • No eating or drinking is allowed in any laboratory under any circumstances

  • A laboratory coat and closed-toe shoes are mandatory

  • Follow safety instructions in your manual and posted in laboratories

  • In case of fire, follow instructions posted outside the laboratory door

  • First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory

  • As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service: unihealth.usyd.edu.au/

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.