# NPLIC: A Machine Learning Approach to Piecewise Linear Interface Construction

arXiv: https://arxiv.org/abs/2007.04244 · HTML: https://mlsanity.com/paper/2007.04244/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [A hybrid inference system for improved curvature estimation in the level-set method using machine learning](https://mlsanity.com/paper/2104.02951/) (2021)
- [Error-correcting neural networks for semi-Lagrangian advection in the level-set method](https://mlsanity.com/paper/2110.11611/) (2021)
- [Error-Correcting Neural Networks for Two-Dimensional Curvature Computation in the Level-Set Method](https://mlsanity.com/paper/2201.12342/) (2022)
- [A Deep Learning Algorithm for Piecewise Linear Interface Construction (PLIC)](https://mlsanity.com/paper/2107.13067/) (2021)
- [Machine learning algorithms for three-dimensional mean-curvature computation in the level-set method](https://mlsanity.com/paper/2208.09047/) (2022)

## Abstract (arXiv)

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.