Learning Vortex Dynamics for Fluid Inference and Prediction
Abstract
We propose a novel differentiable vortex particle (DVP) method to infer and predict fluid dynamics from a single video. Lying at its core is a particle-based latent space to encapsulate the hidden, Lagrangian vortical evolution underpinning the observable, Eulerian flow phenomena. Our differentiable vortex particles are coupled with a learnable, vortex-to-velocity dynamics mapping to effectively capture the complex flow features in a physically-constrained, low-dimensional space. This representation facilitates the learning of a fluid simulator tailored to the input video that can deliver robust, long-term future predictions. The value of our method is twofold: first, our learned simulator enables the inference of hidden physics quantities (e.g., velocity field) purely from visual observation; secondly, it also supports future prediction, constructing the input video's sequel along with its future dynamics evolution. We compare our method with a range of existing methods on both synthetic and real-world videos, demonstrating improved reconstruction quality, visual plausibility, and physical integrity.
Cite
Text
Deng et al. "Learning Vortex Dynamics for Fluid Inference and Prediction." International Conference on Learning Representations, 2023.Markdown
[Deng et al. "Learning Vortex Dynamics for Fluid Inference and Prediction." International Conference on Learning Representations, 2023.](https://mlanthology.org/iclr/2023/deng2023iclr-learning/)BibTeX
@inproceedings{deng2023iclr-learning,
title = {{Learning Vortex Dynamics for Fluid Inference and Prediction}},
author = {Deng, Yitong and Yu, Hong-Xing and Wu, Jiajun and Zhu, Bo},
booktitle = {International Conference on Learning Representations},
year = {2023},
url = {https://mlanthology.org/iclr/2023/deng2023iclr-learning/}
}