QUT offers a diverse range of student topics for Honours, Masters and PhD study. Search to find a topic that interests you or propose your own research topic to a prospective QUT supervisor. You may also ask a prospective supervisor to help you identify or refine a research topic.
Found 3 matching student topics
Displaying 1–3 of 3 results
Physics-informed machine learning
Recent advances in computer vision have demonstrated superhuman performance on a variety of visual tasks including image classification, object detection, human pose estimation and human analysis. However, current approaches for achieving these results center around models that purely learn from large-scale datasets with highly complex neural network architectures. Despite the impressive performance, pure data-driven models usually lack robustness, interpretability, and adherence to physical constraints or commonsense reasoning.As in the real world, the visual world of computer vision is governed by …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Physics-informed reinforcement learning for complex environments, using graph neural networks
Neglecting to incorporate physics information into world models for reinforcement learning leads to reduced adaptability to dynamic and complex environments and overall learning outcomes.In this project, we endeavour to develop and implement learnable models in reinforcement learning (RL) based on graph neural networks (GNNs). These models will integrate object and relation-centric representations to enable accurate predictions, strong generalization, and system identification in complex, dynamical systems. Additionally, we will focus on leveraging extensive world knowledge or physics information to refine representations …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Efficient predictive models using physics-informed machine learning
This research explores how advanced physics-informed neural network models can guide the development of simplified yet accurate predictive systems across scientific and engineering domains. The work spans machine learning, computational physics, and applied mathematics, addressing the critical challenge of creating efficient models that maintain physical consistency and predictive reliability.Recent advances in neural operator learning and physics-informed architectures have demonstrated potential for dramatically reducing model complexity while preserving domain-specific knowledge. This research investigates generalisable frameworks for developing simplified predictive models that …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
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