NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields

Deep learning has shown great potential for modeling the physical dynamics of complex particle systems such as fluids. Existing approaches, however, require the supervision of consecutive particle properties, including positions and velocities. In this paper, we consider a partially observable scena...

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Bibliographic Details
Published inarXiv.org
Main Authors Guan, Shanyan, Deng, Huayu, Wang, Yunbo, Yang, Xiaokang
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 18.06.2022
Subjects
Computational fluid dynamics
Deep learning
Fluid dynamics
Fluid flow
Fluid mechanics
Physical properties
Visual observation
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Summary:Deep learning has shown great potential for modeling the physical dynamics of complex particle systems such as fluids. Existing approaches, however, require the supervision of consecutive particle properties, including positions and velocities. In this paper, we consider a partially observable scenario known as fluid dynamics grounding, that is, inferring the state transitions and interactions within the fluid particle systems from sequential visual observations of the fluid surface. We propose a differentiable two-stage network named NeuroFluid. Our approach consists of (i) a particle-driven neural renderer, which involves fluid physical properties into the volume rendering function, and (ii) a particle transition model optimized to reduce the differences between the rendered and the observed images. NeuroFluid provides the first solution to unsupervised learning of particle-based fluid dynamics by training these two models jointly. It is shown to reasonably estimate the underlying physics of fluids with different initial shapes, viscosity, and densities.
ISSN:2331-8422