Latent-space Dynamics for Reduced Deformable Simulation
| dc.contributor.author | Fulton, Lawson | en_US |
| dc.contributor.author | Modi, Vismay | en_US |
| dc.contributor.author | Duvenaud, David | en_US |
| dc.contributor.author | Levin, David I. W. | en_US |
| dc.contributor.author | Jacobson, Alec | en_US |
| dc.contributor.editor | Alliez, Pierre and Pellacini, Fabio | en_US |
| dc.date.accessioned | 2019-05-05T17:41:37Z | |
| dc.date.available | 2019-05-05T17:41:37Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | We propose the first reduced model simulation framework for deformable solid dynamics using autoencoder neural networks. We provide a data-driven approach to generating nonlinear reduced spaces for deformation dynamics. In contrast to previous methods using machine learning which accelerate simulation by approximating the time-stepping function, we solve the true equations of motion in the latent-space using a variational formulation of implicit integration. Our approach produces drastically smaller reduced spaces than conventional linear model reduction, improving performance and robustness. Furthermore, our method works well with existing force-approximation cubature methods. | en_US |
| dc.description.number | 2 | |
| dc.description.sectionheaders | Learning to Animate | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 38 | |
| dc.identifier.doi | 10.1111/cgf.13645 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 379-391 | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.13645 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf13645 | |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | Computing methodologies | |
| dc.subject | Physical simulation | |
| dc.subject | Dimensionality reduction and manifold learning | |
| dc.title | Latent-space Dynamics for Reduced Deformable Simulation | en_US |