Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time
Authors: name: Jingxuan Xu, name: Hong Huang, name: Chuhang Zou, name: Manolis Savva, name: Yunchao Wei, name: Wuyang Chen

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 reverse 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) 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.