Accelerating Liquid Simulation With an Improved Data‐Driven Method
Accelerating Liquid Simulation With an Improved Data‐Driven Method
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DOI：
10.1111/cgf.14010
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发表时间：
2020-05
影响因子：
2.5
通讯作者：
Yang Gao;Quancheng Zhang;Shuai Li;A. Hao;Hong Qin
中科院分区：
文献类型：
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作者：
Yang Gao;Quancheng Zhang;Shuai Li;A. Hao;Hong Qin
在基于物理的图形应用的液体模拟中，压力投影消耗大量的计算时间，并且经常是计算效率的瓶颈。如何在快速应用压力投影的同时准确捕捉流体的几何形状一直是当前流体模拟研究的热点之一。在本文中，我们将一个人工神经网络到模拟管道处理棘手的投影步骤液体动画。与以往的基于神经网络的气体流动研究相比，本文在代表性特征的组成以及损失函数方面提出了新的进展，以便于具有自由表面边界的流体模拟。具体来说，我们选择速度和水平集函数作为流体状态的附加表示，这不仅允许在神经网络求解器中考虑运动，还允许考虑边界位置。同时，我们利用损失函数中的发散误差进一步模拟了液体的类生命行为。通过这些安排，我们的方法可以大大加快液体模拟中的压力投影步骤，同时保持相当令人信服的视觉结果。此外，我们的神经网络在应用于新的场景合成时表现良好，即使具有不同的边界或尺度。
In physics‐based liquid simulation for graphics applications, pressure projection consumes a significant amount of computational time and is frequently the bottleneck of the computational efficiency. How to rapidly apply the pressure projection and at the same time how to accurately capture the liquid geometry are always among the most popular topics in the current research trend in liquid simulations. In this paper, we incorporate an artificial neural network into the simulation pipeline for handling the tricky projection step for liquid animation. Compared with the previous neural‐network‐based works for gas flows, this paper advocates new advances in the composition of representative features as well as the loss functions in order to facilitate fluid simulation with free‐surface boundary. Specifically, we choose both the velocity and the level‐set function as the additional representation of the fluid states, which allows not only the motion but also the boundary position to be considered in the neural network solver. Meanwhile, we use the divergence error in the loss function to further emulate the lifelike behaviours of liquid. With these arrangements, our method could greatly accelerate the pressure projection step in liquid simulation, while maintaining fairly convincing visual results. Additionally, our neutral network performs well when being applied to new scene synthesis even with varied boundaries or scales.