Title: Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time
Primary area: neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)
Keywords: fluid, simulation, animation, diffusion model

Abstract: We propose a neural physics system for real-time, interactive fluid simulations. Traditional physics-based methods, while accurate, are computationally intensive and suffer from latency issues. Recent machine‑learning methods reduce computational costs while preserving fidelity; yet most still fail to satisfy the latency constraints for real‑time use and lack support for interactive applications. To bridge this gap, we introduce a novel hybrid method that integrates numerical simulation, neural physics, and generative control. Our neural physics jointly pursues low-latency simulation and high physical fidelity by employing a fallback safeguard to classical numerical solvers.
Furthermore, we develop a diffusion-based controller that is trained using a revserve modeling strategy to generate external dynamic force fields for fluid manipulation. Our system demonstrates robust performance across diverse 2D/3D scenarios, material types, and obstacle interactions, achieving real-time simulations at high frame rates (11~29% latency reduced) while enabling fluid control guided by
user-friendly freehand sketches. We present a significant step towards practical, controllable, and physically plausible fluid simulations for real-time interactive applications. We promise to release both models and data upon acceptance.

TL;DR: We propose a neural physics system for real-time, interactive fluid simulations.