Deep Learning Gaussian Processes For Computer Models with Heteroskedastic and High-Dimensional Outputs

Laura Schultz; Vadim Sokolov

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arXiv · 2209.02163

Deep Learning Gaussian Processes For Computer Models with Heteroskedastic and High-Dimensional Outputs

Laura Schultz·Vadim Sokolov

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Abstract

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.

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Laura Schultz, Vadim Sokolov. 2022-09-05. Deep Learning Gaussian Processes For Computer Models with Heteroskedastic and High-Dimensional Outputs. https://arxiv.org/abs/2209.02163

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