Gaussian processes (GPs) serve as flexible surrogates for complex surfaces, but buckle under the cubic cost of matrix decompositions with big training data sizes. Geospatial and machine learning communities suggest pseudo-inputs, or inducing points, as one strategy to obtain an approximation easing that computational burden. However, we show how placement of inducing points and their multitude can be thwarted by pathologies, especially in large-scale dynamic response surface modeling tasks. As remedy, we suggest porting the inducing point idea, which is usually applied globally, over to a more local context where selection is both easier and faster. In this way, our proposed methodology hybridizes global inducing point and data subset-based local GP approximation. A cascade of strategies for planning the selection of local inducing points is provided, and comparisons are drawn to related methodology with emphasis on computer surrogate modeling applications. We show that local inducing points extend their global and data subset component parts on the accuracy–computational efficiency frontier. Illustrative examples are provided on benchmark data and a large-scale real-simulation satellite drag interpolation problem.

高斯过程（GPs）可作为复杂表面的灵活替代物，但在具有大训练数据量的矩阵分解的三次成本下屈服。地理空间和机器学习社区建议使用伪输入或归纳点作为获得近似值的一种策略，从而减轻了计算负担。但是，我们展示了如何通过病理来阻止诱导点的放置及其众多，特别是在大规模动态响应曲面建模任务中。作为补救措施，我们建议将通常在全球范围内应用的归纳点概念移植到更容易，更快捷选择的更本地化的环境中。通过这种方式，我们提出的方法将全局归纳点和基于数据子集的局部GP近似混合在一起。提供了一系列用于规划局部诱导点选择的策略，并与相关方法进行了比较，重点是计算机替代建模应用程序。我们表明，局部归纳点在精度-计算效率边界上扩展了它们的全局子集和数据子集组成部分。提供了有关基准数据和大规模实际模拟卫星阻力插值问题的说明性示例。