NPLIC: A Machine Learning Approach to Piecewise Linear Interface Construction

TL;DR

This paper introduces NPLIC, a neural network-based method for efficient and accurate piecewise linear interface construction in fluid simulations, reducing computational cost across various mesh types.

Contribution

The paper presents a novel neural network approach for PLIC that is faster and easier to implement than traditional geometric methods, applicable to multiple mesh geometries.

Findings

Achieves accurate interface reconstruction with reduced computational time.

Works effectively across different mesh types including square, cubic, triangular, and tetrahedral.

Demonstrates the potential of data-driven methods in fluid interface tracking.

Abstract

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.