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Unit of study_
PHYS4015: The Physics of Complex Systems
2025 unit information
Complex systems are pivotal in understanding phenomena that transcend the capabilities of their individual components. This field studies diverse systems characterized by intricate interactions and dependencies, leading to novel behaviours such as self-organization and emergence. These phenomena are not predictable by examining the components alone. This unit offers a deep dive into the fundamental physics of complex systems, focusing on their dynamics, structure, and the governing principles of their behaviour. It provides a thorough introduction to key concepts in complex systems, including chaos, self-organized criticality, synchronization, and pattern formation outside of equilibrium. These concepts are crucial for understanding the complex and emergent behaviours observed across various systems. Additionally, this course applies complex systems theory to two cutting-edge fields: neuroscience and artificial intelligence (AI). In neuroscience, modelling the brain's complex systems enhances our understanding of how perception and cognition emerge from neural circuits. In AI, applying principles of complex systems can improve our understanding and enhance the efficacy of deep learning networks. Students in this course will develop skills in modelling, quantitative analysis, and critical thinking. These skills are vital for dissecting complex systems in both academic research and practical applications, preparing students to address challenges in advanced scientific and technological fields.
Unit details and rules
Managing faculty or University school:
Science
| Study level | Undergraduate |
|---|---|
| Academic unit | Physics Academic Operations |
| Credit points | 6 |
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Prerequisites:
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144 credit points of units including (MATH1X01 or MATH1X21 or MATH1906 or MATH1931) and MATH1X02 |
|---|---|
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Corequisites:
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None |
| Prohibitions:
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None |
| Assumed knowledge:
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First- and second-year physics |
At the completion of this unit, you should be able to:
- LO1. Demonstrate an understanding of key concepts in neural dynamics and computation.
- LO2. Apply these concepts to develop models, and to solve qualitative and quantitative problems in scientific contexts, using appropriate mathematical and computing techniques as necessary.
- LO3. Design brain-inspired algorithms to solve problems.
- LO4. Develop models to explain neurophysiological and/or psychophysical data.
- LO5. Communicate scientific information appropriately, through written work.
- LO6. Analyse the dynamical process of brain functions such as decision making and develop appropriate quantitative methods for solving them.
- LO7. Demonstrate a sense of responsibility, ethical behaviour, and independence as a learner and as a scientist.
Unit availability
This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.
The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
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Semester 2 2025
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Normal day | Camperdown/Darlington, Sydney |
Outline unavailable
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Modes of attendance (MoA)
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