Volume of fluid (VOF) methods are extensively used to track fluid interfaces in numerical simulations, and many VOF algorithms require that the interface be reconstructed geometrically. For this purpose, the Piecewise Linear Interface Construction (PLIC) technique is most frequently used, which for reasons of geometric complexity can be slow and difficult to implement. Here, we propose an alternative neural network based method called NPLIC to perform PLIC calculations. The model is trained on a large synthetic dataset of PLIC solutions for square, cubic, triangular, and tetrahedral meshes. We show that this data-driven approach results in accurate calculations at a fraction of the usual computational cost, and a single neural network system can be used for interface reconstruction of different mesh types.

流体体积（VOF）方法被广泛用于在数值模拟中跟踪流体界面，许多VOF算法要求对界面进行几何重构。为此，最常用的是分段线性接口构造（PLIC）技术，由于几何复杂性的原因，这种技术可能比较缓慢且难以实现。在这里，我们提出了一种称为NPLIC的基于神经网络的替代方法来执行PLIC计算。该模型在正方形，立方，三角形和四面体网格的PLIC解决方案的大型综合数据集上训练。我们表明，这种数据驱动的方法可以以通常计算量的一小部分进行准确的计算，并且单个神经网络系统可以用于不同网格类型的界面重建。