The Geometry of r-adaptive meshes generated using Optimal Transport Methods

TL;DR

This paper analyzes how optimal transport-based mesh redistribution methods generate anisotropic meshes aligned with features, deriving the underlying metric tensor and demonstrating its effectiveness through theoretical analysis and numerical experiments.

Contribution

It derives the explicit form of the metric tensor associated with r-adaptive meshes generated by optimal transport, linking anisotropy to the density function and feature curvature.

Findings

Eigenvectors are orthogonal and tangent to features.

Eigenvalue ratios depend on density and curvature.

Numerical results confirm theoretical predictions.

Abstract

The principles of mesh equidistribution and alignment play a fundamental role in the design of adaptive methods, and a metric tensor M and mesh metric are useful theoretical tools for understanding a methods level of mesh alignment, or anisotropy. We consider a mesh redistribution method based on the Monge-Ampere equation, which combines equidistribution of a given scalar density function with optimal transport. It does not involve explicit use of a metric tensor M, although such a tensor must exist for the method, and an interesting question to ask is whether or not the alignment produced by the metric gives an anisotropic mesh. For model problems with a linear feature and with a radially symmetric feature, we derive the exact form of the metric M, which involves expressions for its eigenvalues and eigenvectors. The eigenvectors are shown to be orthogonal and tangential to the feature, and the ratio of the eigenvalues (corresponding to the level of anisotropy) is shown to depend, both locally and globally, on the value of the density function and the amount of curvature. We thereby demonstrate how the optimal transport method produces an anisotropic mesh along a given feature while equidistributing a suitably chosen scalar density function. Numerical results are given to verify these results and to demonstrate how the analysis is useful for problems involving more complex features, including for a non-trivial time dependant nonlinear PDE which evolves narrow and curved reaction fronts.