Learning Vortex Dynamics for Fluid Inference and Prediction
- URL: http://arxiv.org/abs/2301.11494v1
- Date: Fri, 27 Jan 2023 02:10:05 GMT
- Title: Learning Vortex Dynamics for Fluid Inference and Prediction
- Authors: Yitong Deng, Hong-Xing Yu, Jiajun Wu, Bo Zhu
- Abstract summary: We propose a novel machine learning method based on differentiable vortex particles to infer and predict fluid dynamics from a single video.
We devise a novel differentiable vortex particle system in conjunction with their learnable, vortex-to-velocity dynamics mapping to effectively capture and represent the complex flow features in a reduced space.
- Score: 25.969713036393895
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose a novel machine learning method based on differentiable vortex
particles to infer and predict fluid dynamics from a single video. The key
design of our system is a particle-based latent space to encapsulate the
hidden, Lagrangian vortical evolution underpinning the observable, Eulerian
flow phenomena. We devise a novel differentiable vortex particle system in
conjunction with their learnable, vortex-to-velocity dynamics mapping to
effectively capture and represent the complex flow features in a reduced space.
We further design an end-to-end training pipeline to directly learn and
synthesize simulators from data, that can reliably deliver future video
rollouts based on limited observation. 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, to be used for motion
analysis; secondly, it also supports future prediction, constructing the input
video's sequel along with its future dynamics evolution. We demonstrate our
method's efficacy by comparing quantitatively and qualitatively with a range of
existing methods on both synthetic and real-world videos, displaying improved
data correspondence, visual plausibility, and physical integrity.
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