Computer experiments with both quantitative and qualitative inputs have become common across various areas. However, constructing accurate and computationally efficient emulators for such experiments at large scales remains a significant challenge. We propose a novel, scalable framework for emulating computer experiments with mixed inputs. Our approach is based on a new covariance function integrating additive Gaussian Processes (GPs) to handle the mixed inputs, with Vecchia approximation for scalability. We demonstrate that methods for large-scale computer experiments can be effectively extended when paired with our proposed modeling framework.

翻译：兼具定量与定性输入的数值实验已广泛出现在各个领域，然而，在大规模场景下构建精确且计算高效的仿真器仍面临重大挑战。本文提出了一种新颖且可扩展的框架，用于仿真包含混合输入的数值实验。该方法基于一种新型协方差函数，该函数结合了加性高斯过程来处理混合输入，并采用维基亚近似实现可扩展性。我们证明，当与所提出的建模框架配合使用时，大规模数值实验中的方法可以有效地扩展应用。