IMOSOpen OpportunitiesResearchers have started to explore data-driven physics simulations, particularly with Graph Neural Networks for rigid objects collisions. However, simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex geometry and the strong non-linearity of the interactions. In this project, you will focus on the task of learning/simulating rigid objects dynamics with Graph
Neural Networks (GNNs), with the end-goal of predicting future or alternative trajectories for physical rigid objects in a scene. - Engineering and Technology, Information, Computing and Communication Sciences
- Master Thesis, Semester Project
| Understanding the dynamics of rigid object interactions is crucial in various fields, including robotics, computer graphics, and physics-based simulations. In this project, you will focus on the task of learning/simulating rigid objects dynamics from videos, with the end-goal of predicting future or alternative trajectories for the objects in the scene. - Engineering and Technology, Information, Computing and Communication Sciences
- Master Thesis, Semester Project
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