Generating Liquid Simulations with Deformation-aware Neural Networks

By Lukas Prantl, Boris Bonev, Nils Thürey. 2017

16 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yaser Afshar
2. Shaowu Pan
3. Saakaar Bhatnagar
4. S. Kaushik
5. Karthik Duraisamy
6. Shuhao Cao
7. Han Gao
8. Heng Xiao

Citations in the corpus

• Data-driven synthesis of smoke flows with CNN-based feature descriptors (2017, unknown): “…have demonstrated the inference of SPH forces with regression forests [Ladicky et al. 2015], proposed CNNs for fast pressure projections [Tompson et al. 2016], learned space-time deformations for interactive liquids [Prantl et al. 2017] , and modeled splash statistics with NNs [Um et al. 2017].”
• Prediction of aerodynamic flow fields using convolutional neural networks (2019, methodology): “this study, to capture shape details in different object representations, and following [12,27], we use the SDF sampled on a Cartesian grid.”