用神经网络构建流体运动基底，实现高效实时流体动画。
Neural Kinematic Bases for Fluids
- 用MLP表示流体速度场的运动基底，无需网格。
- 通过损失函数确保基底满足无散、正交等物理特性。
- 可实时动画化草图流场，适用于复杂边界与3D场景。
我们提出一种无网格流体模拟方法，利用由MLP表示的运动学神经基底来建模速度场。设计了一组损失函数，确保这些神经基底能近似满足正交性、无散度、边界对齐和光滑性等基本物理特性。由此生成的神经基底可用于拟合输入的流场草图，使拟合结果继承相同的物理属性。随后可使用标准时间积分器实现实时动画。该方法支持不同域、移动边界，并可自然扩展至三维空间。
原文摘要 · Abstract (English)
We propose mesh-free fluid simulations that exploit a kinematic neural basis for velocity fields represented by an MLP. We design a set of losses that ensures that these neural bases approximate fundamental physical properties such as orthogonality, divergence-free, boundary alignment, and smoothness. Our neural bases can then be used to fit an input sketch of a flow, which will inherit the same fundamental properties from the bases. We then can animate such flow in real-time using standard time integrators. Our neural bases can accommodate different domains, moving boundaries, and naturally extend to three dimensions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。