Outline

Overview

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.

Unit details and rules

Academic unit	Physics Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	COSC1003 or COSC1903 or COSC2902
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

Type	Description	Weight	Due	Length
Final exam (Record+)	Final Exam Open book final exam, with automated proctoring.	40%	Formal exam period	2 hours
Outcomes assessed:
Tutorial quiz	Class test 1 In-lab open-book practical test	20%	Week 07	90 minutes
Outcomes assessed:
Assignment	Assignment Written report	10%	Week 12	Two weeks
Outcomes assessed:
Tutorial quiz	Class test 2 In-lab open-book practical test	20%	Week 13	90 minutes
Outcomes assessed:
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:
= Type B final exam ?

Assessment summary

Weekly lab submissions: work through a Jupyter notebook and submit two lab checkpoints per lab
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: The exam will follow a similar format to the class tests, covering all the material of the course, however submission will be in a formal exam booklet rather than a Jupyter notebook

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.

Learning support

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.

Weekly schedule

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

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

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.

Outcomes
Graduate qualities

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
Learning outcomes

Responding to student feedback

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

No major changes have been made since this unit was last offered, however a number of small changes have been made to improve online accessibility and student support.

Additional information

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

COSC2002: Computational Modelling

Semester 1, 2021 [Normal day] - Remote

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect