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

TL;DR

This paper introduces Deep Learning Gaussian Processes (DL-GP), a novel approach combining deep learning and Gaussian Processes to efficiently analyze complex computer models with heteroskedastic and high-dimensional outputs.

Contribution

The paper presents a new methodology that integrates deep learning transformations with Gaussian Processes to handle complex, high-dimensional, and heteroskedastic data from computer simulations.

Findings

Effective modeling of heteroskedastic outputs

Application to motorcycle accident and Ebola outbreak simulations

Potential for broad application in various scientific fields

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.