标题： PSI：构造自定义单纯形以插值高维无结构数据 显示英文标题

标题： PSI: Constructing ad-hoc Simplices to Interpolate High-Dimensional Unstructured Data

摘要： 使用重心坐标插值非结构化数据在高维情况下变得不可行，因为构建Delaunay三角剖分需要的内存需求过于庞大。 我们提出了一种新的算法，用于构建自定义单纯形，并通过最近邻启发式方法和迭代降维投影，实证证明这些单纯形可以包含目标坐标。 我们利用这些单纯形来插值天体物理冷却函数$\Lambda$，并表明这一新方法仅需之前所需内存的一小部分即可获得良好的结果。 显示英文摘要

摘要： 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.