FIT3139 – Computational modelling and simulation
This unit provides an overview of computational science and an introduction to its central methods. It covers the role of computational tools and methods in 21st century science, emphasising modelling and simulation. It introduces a variety of models, providing contrasting studies on: continuous versus discrete models; analytical versus numerical models; deterministic versus stochastic models; and static versus dynamic models. Other topics include: Monte-Carlo methods; epistemology of simulations; visualisation; high-dimensional data analysis; optimisation; limitations of numerical methods; high-performance computing and data-intensive research.
A general overview is provided for each main topic, followed by a detailed technical exploration of one or a few methods selected from the area. These are applied in tutorials and laboratories which also acquaint students with standard scientific computing software (e.g., Mathematica, Matlab, Maple, Sage). Applications are drawn from disciplines including Physics, Biology, Bioinformatics, Chemistry, Social Science.
Faculty of Information Technology
Owning organisational unit:
Faculty of Information Technology
Study level:
Offerings S1-01-CLAYTON-ON-CAMPUS
Location: Clayton
Teaching period: First semester Attendance mode: On-campus
Rules Enrolment Rule
Prerequisites: One of MAT1841, ENG1091, ENG1005, MTH1030, MTH1035 or equivalent, plus any introductory programming unit (e.g. FIT1045, FIT1048, FIT1051, FIT1053, ECE2071, or equivalent)
Undergraduate
Open to exchange or study abroad students?
Credit points:
Chief Examiner(s)
Dr Julian Garcia Gallego
CS Help, Email: tutorcs@163.com
Learning outcomes
On successful completion of this unit, you should be able to:
1. Explain and apply the process of computational scientific model building, verification and interpretation;
2. Analyse the differences between core classes of modelling approaches (Numerical versus Analytical; Linear versus Non-linear; Continuous versus Discrete; Deterministic versus Stochastic);
3. Evaluate the implications of choosing different modelling approaches;
4. Rationalise the role of simulation and data visualisation in science;
5. Apply all of the above to solving idealisations of real-world problems across various scientific disciplines.
Teaching approach Case-based teaching
The teaching and learning approach provides facilitated learning and practical exploration of case studies to develop real-world skills.
Offering(s):
Applies to all offerings
Assessment Assignment Part 1
Computer Science Tutoring
Value %: 15
Assignment Part 2 Value %: 25
Final Project Value %: 50
Value %: 10
Scheduled teaching activities Laboratories
Total hours: 22 hours Offerings:
Applies to all offerings
Total hours: 22 hours Offerings:
Applies to all offerings
Total hours: 24 hours
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Workload requirements Workload
Tutorials and labs start from Week 2 of the Semester.
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.
Availability in areas of study
Advanced computer science Computational science
Data science
Offerings:
Applies to all offerings