Liquid Splash Modeling with Neural Networks
| dc.contributor.author | Um, Kiwon | en_US |
| dc.contributor.author | Hu, Xiangyu | en_US |
| dc.contributor.author | Thuerey, Nils | en_US |
| dc.contributor.editor | Thuerey, Nils and Beeler, Thabo | en_US |
| dc.date.accessioned | 2018-07-23T10:07:45Z | |
| dc.date.available | 2018-07-23T10:07:45Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | This paper proposes a new data-driven approach to model detailed splashes for liquid simulations with neural networks. Our model learns to generate small-scale splash detail for the fluid-implicit-particle method using training data acquired from physically parametrized, high resolution simulations. We use neural networks to model the regression of splash formation using a classifier together with a velocity modifier. For the velocity modification, we employ a heteroscedastic model. We evaluate our method for different spatial scales, simulation setups, and solvers. Our simulation results demonstrate that our model significantly improves visual fidelity with a large amount of realistic droplet formation and yields splash detail much more efficiently than finer discretizations. | en_US |
| dc.description.number | 8 | |
| dc.description.sectionheaders | Fluids with Particles | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 37 | |
| dc.identifier.doi | 10.1111/cgf.13522 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 171-182 | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.13522 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf13522 | |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | Computing methodologies | |
| dc.subject | Physical simulation | |
| dc.subject | Supervised learning by regression | |
| dc.title | Liquid Splash Modeling with Neural Networks | en_US |