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

Understand

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.