PSI: Constructing ad-hoc Simplices to Interpolate High-Dimensional Unstructured Data
arXiv:2109.13926 · doi:10.1016/j.jcp.2022.111476
Abstract
Interpolating unstructured data using barycentric coordinates becomes infeasible at high dimensions due to the prohibitive memory requirements of building a Delaunay triangulation. We present a new algorithm to construct ad-hoc simplices that are empirically guaranteed to contain the target coordinates, based on a nearest neighbor heuristic and an iterative dimensionality reduction through projection. We use these simplices to interpolate the astrophysical cooling function and show that this new approach produces good results with just a fraction of the previously required memory.
4 pages, 4 figures