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Found 2 matching student topics

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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

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