A Pioneering Neural Network Method for Efficient and Robust Fluid Simulation

2024-12-14

AI Summary

Traditional fluid simulation methods (e.g., SPH, Flow3D) suffer from high computational cost and insufficient accuracy/stability in complex scenes. To address this, this paper proposes a novel, efficient, and robust neural fluid simulation method tailored for computer graphics and real-time game animation, framing fluid motion as point cloud transformation—the first such approach. Our contributions are threefold: (1) a deep learning framework for stable fluid particle dynamics; (2) a triangular geometric feature fusion module that jointly encodes local dynamics, momentum conservation, and global stability constraints; and (3) integration of physics-informed priors to predict dynamic particle trajectories. Experiments demonstrate that our method achieves significantly higher accuracy than state-of-the-art neural fluid models, with inference speeds approximately 10× faster than SPH and over 300× faster than Flow3D—while maintaining high fidelity and real-time performance.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Neuro-Symbolic Learning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Fluid simulation is an important research topic in computer graphics (CG) and animation in video games. Traditional methods based on Navier-Stokes equations are computationally expensive. In this paper, we treat fluid motion as point cloud transformation and propose the first neural network method specifically designed for efficient and robust fluid simulation in complex environments. This model is also the deep learning model that is the first to be capable of stably modeling fluid particle dynamics in such complex scenarios. Our triangle feature fusion design achieves an optimal balance among fluid dynamics modeling, momentum conservation constraints, and global stability control. We conducted comprehensive experiments on datasets. Compared to existing neural network-based fluid simulation algorithms, we significantly enhanced accuracy while maintaining high computational speed. Compared to traditional SPH methods, our speed improved approximately 10 times. Furthermore, compared to traditional fluid simulation software such as Flow3D, our computation speed increased by more than 300 times.

Problem

• Complex Fluid Dynamics
• Navier-Stokes Equations
• Computational Efficiency

Innovation

• Neural Network-based Fluid Simulation
• Complex Fluid Dynamics
• High Computational Efficiency