NeuroFluid：基于粒子驱动神经辐射场的流体动力学
NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields
摘要：
深度学习在模拟流体等复杂粒子系统的物理动力学方面显示出巨大的潜力。然而，现有的方法需要监督连续的粒子属性，包括位置和速度。在本文中，我们考虑了一个部分可观察的情况下，被称为流体动力学接地，也就是说，推断流体粒子系统内的状态转换和相互作用，从连续的视觉观察的流体表面。我们提出了一个名为NeuroFluid的可微两阶段网络。我们的方法包括（i）粒子驱动的神经渲染器，它涉及到流体的物理性质到体渲染功能，和（ii）粒子过渡模型优化，以减少渲染和观察到的图像之间的差异。NeuroFluid通过联合训练这两个模型，为基于粒子的流体动力学的无监督学习提供了第一个解决方案。它表明，合理地估计不同的初始形状，粘度和密度的流体的基本物理。
英文摘要：
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.
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