论文标题
神经流体：用粒子驱动的神经辐射场接地的流体动力学
NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields
论文作者
论文摘要
深度学习显示了建模复杂粒子系统（例如流体）的物理动力学的巨大潜力。但是，现有方法需要对连续粒子特性的监督，包括位置和速度。在本文中，我们考虑了一种被称为流体动力学接地的部分可观察的方案，即从流体表面的顺序视觉观察中推断出流体粒子系统内的状态过渡和相互作用。我们提出了一个名为Neurofluid的可区分的两阶段网络。我们的方法由（i）粒子驱动的神经渲染器组成，该神经渲染器涉及流体物理特性到体积渲染函数中，以及（ii）优化的粒子过渡模型，以减少渲染和观察到的图像之间的差异。 Neurofluid通过共同训练这两个模型，为对基于粒子的流体动力学的学习提供了第一个解决方案。证明可以合理估计具有不同初始形状，粘度和密度不同的流体的基本物理。
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.
