PSI: Constructing ad-hoc Simplices to Interpolate High-Dimensional
  Unstructured Data
  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 $\Lambda$ and show that this new
approach produces good results with just a fraction of the previously required
memory.
