# Nonstationary Gaussian Process Surrogates

arXiv: https://arxiv.org/abs/2305.19242 · HTML: https://mlsanity.com/paper/2305.19242/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2023.

## Built on by (most cited first, 8 of 8)

- [A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes](https://mlsanity.com/paper/2309.10068/) (2023)
- [All Emulators are Wrong, Many are Useful, and Some are More Useful Than Others: A Reproducible Comparison of Computer Model Surrogates](https://mlsanity.com/paper/2512.09060/) (2025)
- [Modular Jump Gaussian Processes](https://mlsanity.com/paper/2505.15557/) (2025)
- [Deep Jump Gaussian Processes for Surrogate Modeling of High-Dimensional Piecewise Continuous Functions](https://mlsanity.com/paper/2510.21974/) (2025)
- [Sparse Techniques for Regression in Deep Gaussian Processes](https://mlsanity.com/paper/2505.11355/) (2025)
- [Applied metamodelling for ATM performance simulations](https://mlsanity.com/paper/2308.03404/) (2023)
- [Actively Learning Joint Contours of Multiple Computer Experiments](https://mlsanity.com/paper/2512.13530/) (2025)
- [Covariance-Boosted Gaussian Processes for Spatiotemporal Irregularities](https://mlsanity.com/paper/2607.23018/) (2026)

## Abstract (arXiv)

We provide a survey of nonstationary surrogate models which utilize Gaussian processes (GPs) or variations thereof, including nonstationary kernel adaptations, partition and local GPs, and spatial warpings through deep Gaussian processes. We also overview publicly available software implementations and conclude with a bake-off involving an 8-dimensional satellite drag computer experiment. Code for this example is provided in a public git repository.