PSI: Constructing ad-hoc Simplices to Interpolate High-Dimensional Unstructured Data
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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 Λ and show that this new approach clearly outperforms our previous implementation at high dimensions.
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