Learning phase field mean curvature flows with neural networks

TL;DR

This paper presents neural network-based numerical methods for approximating mean curvature flows of surfaces, capable of handling both oriented and non-orientable cases, with strong generalization to complex and constrained scenarios.

Contribution

The authors develop neural network approaches inspired by splitting schemes for the Allen-Cahn equation, extending to non-orientable interfaces and complex applications.

Findings

Networks generalize well to complex interfaces and singularities.

Method effectively handles volume constraints and multiphase flows.

Approach is flexible for various geometric and physical applications.

Abstract

We introduce in this paper new and very effective numerical methods based on neural networks for the approximation of the mean curvature flow of either oriented or non-orientable surfaces. To learn the correct interface evolution law, our neural networks are trained on phase field representations of exact evolving interfaces. The structures of the networks draw inspiration from splitting schemes used for the discretization of the Allen-Cahn equation. But when the latter approximate the mean curvature motion of oriented interfaces only, the approach we propose extends very naturally to the non-orientable case. Through a variety of examples, we show that our networks, trained only on flows of smooth and simplistic interfaces, generalize very well to more complex interfaces, either oriented or non-orientable, and possibly with singularities. Furthermore, they can be coupled easily with additional constraints which opens the way to various applications illustrating the flexibility and effectiveness of our approach: mean curvature flows with volume constraint, multiphase mean curvature flows, numerical approximation of Steiner trees or minimal surfaces.