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arXiv · 2505.18926

Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time

Jingxuan Xu·Hong Huang·Chuhang Zou·Manolis Savva·Yunchao Wei·Wuyang Chen
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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 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.

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Jingxuan Xu, Hong Huang, Chuhang Zou, Manolis Savva, Yunchao Wei, Wuyang Chen. 2025-05-25. Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time. https://arxiv.org/abs/2505.18926

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