# Linked Deep Gaussian Process Emulation for Model Networks

arXiv: https://arxiv.org/abs/2306.01212 · HTML: https://mlsanity.com/paper/2306.01212/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 2 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [Doubly Stochastic Variational Inference for Deep Gaussian Processes](https://mlsanity.com/paper/1705.08933/) (2017)
- [Active Learning for Deep Gaussian Process Surrogates](https://mlsanity.com/paper/2012.08015/) (2020)
- [Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo](https://mlsanity.com/paper/1806.05490/) (2018)
- [Deep Gaussian Process Emulation using Stochastic Imputation](https://mlsanity.com/paper/2107.01590/) (2021)
- [Computer emulation with non-stationary Gaussian processes](https://mlsanity.com/paper/1308.4756/) (2013)
- [Diagnostic-Driven Nonstationary Emulators Using Kernel Mixtures](https://mlsanity.com/paper/1803.04906/) (2018)

## Abstract (arXiv)

Modern scientific problems are often multi-disciplinary and require integration of computer models from different disciplines, each with distinct functional complexities, programming environments, and computation times. Linked Gaussian process (LGP) emulation tackles this challenge through a divide-and-conquer strategy that integrates Gaussian process emulators of the individual computer models in a network. However, the required stationarity of the component Gaussian process emulators within the LGP framework limits its applicability in many real-world applications. In this work, we conceptualize a network of computer models as a deep Gaussian process with partial exposure of its hidden layers. We develop a method for inference for these partially exposed deep networks that retains a key strength of the LGP framework, whereby each model can be emulated separately using a DGP and then linked together. We show in both synthetic and empirical examples that our linked deep Gaussian process emulators exhibit significantly better predictive performance than standard LGP emulators in terms of accuracy and uncertainty quantification. They also outperform single DGPs fitted to the network as a whole because they are able to integrate information from the partially exposed hidden layers. Our methods are implemented in an R package textttdgpsi that is freely available on CRAN.