用神经网络模拟流体，速度比传统方法快300倍。
A Pioneering Neural Network Method for Efficient and Robust Fluid Simulation
- 将流体运动视为点云变换，设计专用神经网络模型。
- 在复杂场景中实现稳定流体动力学建模，精度更高。
- 适合游戏与动画领域，对实时流体仿真有重要意义。
流体模拟是计算机图形学和视频游戏动画中的重要研究课题。基于纳维-斯托克斯方程的传统方法计算成本高昂。本文将流体运动视为点云变换，提出首个专为复杂环境下的高效且鲁棒流体模拟设计的神经网络方法。该模型也是首个能在复杂场景中稳定建模流体粒子动力学的深度学习模型。通过三角形特征融合设计，实现了流体动力学建模、动量守恒约束与全局稳定性控制之间的最优平衡。我们在多个数据集上进行了全面实验。相比现有基于神经网络的流体模拟算法，本方法显著提升精度并保持高计算速度；相比传统SPH方法，速度提升约10倍；相较于传统流体模拟软件Flow3D，计算速度提升超过300倍。
原文摘要 · Abstract (English)
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.
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