Deep Learning Gaussian Processes (DL-GP) are proposed as a methodology for analyzing (approximating) computer models that produce heteroskedastic and high-dimensional output. Computer simulation models have many areas of applications, including social-economic processes, agriculture, environmental, biology, engineering and physics problems. A deterministic transformation of inputs is performed by deep learning and predictions are calculated by traditional Gaussian Processes. We illustrate our methodology using a simulation of motorcycle accidents and simulations of an Ebola outbreak. Finally, we conclude with directions for future research.

翻译：提出了深层学习高斯进程(DL-GP),作为分析(接近)产生超导和高维输出的计算机模型的方法;计算机模拟模型有许多应用领域,包括社会经济进程、农业、环境、生物、工程和物理问题;通过深层学习和预测对投入进行决定性的转变,由传统的高斯进程进行计算;我们用摩托车事故模拟和埃博拉爆发模拟来说明我们的方法;最后,我们最后提出了未来研究的方向。