Applications of graph neural networks in simulation vs. animation
| dc.contributor.author | Shatzel, Liam | |
| dc.date.accessioned | 2025-04-28T18:54:16Z | |
| dc.date.available | 2025-04-28T18:54:16Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Graph neural networks (GNNs) provide a method, using graphs, to organize data which is not typically suited for a matrix or linear structure, allowing it to be learned in a neural network. GNNs have applications in drug discovery, recommendation systems, social networks, and physics simulations. This research focuses on analyzing the applications of GNNs for physical systems from an animation and simulation viewpoint. Simulation is the modelling of dynamic systems using physics, whereas animation is a sequence of images played to depict motion. In this work we first find the best hyperparameters for each use-case, then compare the mean squared error, model size, and rollout speed of each. Exploring the effectiveness of GNNs, when applied to animation and simulation, demonstrates their capabilities in media and physics. This also serves as a starting point for the applicability of GNNs to different domains. | |
| dc.description.reviewstatus | Reviewed | |
| dc.description.scholarlevel | Undergraduate | |
| dc.description.sponsorship | Jamie Cassels Undergraduate Research Awards (JCURA) | |
| dc.identifier.uri | https://hdl.handle.net/1828/22031 | |
| dc.language.iso | en | |
| dc.publisher | University Of Victoria | |
| dc.subject | physics simulation | |
| dc.subject | animation | |
| dc.subject | quantitative analysis | |
| dc.subject | machine learning | |
| dc.subject | computer graphics | |
| dc.title | Applications of graph neural networks in simulation vs. animation | |
| dc.type | Poster |