混合神经-MPM实现实时交互式流体模拟

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

May 25, 2025

作者: Jingxuan Xu, Hong Huang, Chuhang Zou, Manolis Savva, Yunchao Wei, Wuyang Chen

cs.AI

摘要

我们提出了一种用于实时交互式流体模拟的神经物理系统。传统的基于物理的方法虽然精确，但计算量大且存在延迟问题。近期的机器学习方法在保持保真度的同时降低了计算成本；然而，大多数方法仍无法满足实时使用的延迟要求，且缺乏对交互应用的支持。为弥合这一差距，我们引入了一种新颖的混合方法，该方法整合了数值模拟、神经物理和生成控制。我们的神经物理系统通过采用经典数值求解器的备用保障机制，同时追求低延迟模拟和高物理保真度。此外，我们开发了一种基于扩散的控制器，该控制器采用逆向建模策略进行训练，以生成用于流体操控的外部动态力场。我们的系统在多样化的2D/3D场景、材料类型及障碍物交互中展现出稳健性能，实现了高帧率下的实时模拟（延迟为11~29%），并支持通过用户友好的手绘草图引导流体控制。我们朝着实用、可控且物理可信的实时交互式流体模拟迈出了重要一步。我们承诺在论文被接受后发布模型和数据。

English

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.