# Deep Multi-fidelity Gaussian Processes

arXiv: https://arxiv.org/abs/1604.07484 · HTML: https://mlsanity.com/paper/1604.07484/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2016.

## Built on by (most cited first, 15 of 15)

- [Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities](https://mlsanity.com/paper/2102.13403/) (2021)
- [Multi-fidelity surrogate modeling using long short-term memory networks](https://mlsanity.com/paper/2208.03115/) (2022)
- [Multi-fidelity Hierarchical Neural Processes](https://mlsanity.com/paper/2206.04872/) (2022)
- [Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling](https://mlsanity.com/paper/2402.18846/) (2024)
- [Disentangled Multi-Fidelity Deep Bayesian Active Learning](https://mlsanity.com/paper/2305.04392/) (2023)
- [Deep convolutional neural network for shape optimization using level-set approach](https://mlsanity.com/paper/2201.06210/) (2022)
- [Open Problems in Applied Deep Learning](https://mlsanity.com/paper/2301.11316/) (2023)
- [On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiological boundary conditions](https://mlsanity.com/paper/2506.11683/) (2025)
- [Multi-Fidelity SINDy: Sparse Discovery of Nonlinear Dynamical Systems with Fidelity-Weighted Measurements](https://mlsanity.com/paper/2606.15690/) (2026)
- [Gradient-enhanced multifidelity neural networks for high-dimensional function approximation](https://mlsanity.com/paper/2103.12247/) (2021)
- [Physics-informed reinforcement learning via probabilistic co-adjustment functions](https://mlsanity.com/paper/2309.05404/) (2023)
- [Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression](https://mlsanity.com/paper/2403.09758/) (2024)
- [Multi-modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation](https://mlsanity.com/paper/2509.21711/) (2025)
- [A Constrained Multi-Fidelity Bayesian Optimization Method](https://mlsanity.com/paper/2510.10984/) (2025)
- [Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data](https://mlsanity.com/paper/2603.22050/) (2026)

## Abstract (arXiv)

We develop a novel multi-fidelity framework that goes far beyond the classical AR(1) Co-kriging scheme of Kennedy and O'Hagan (2000). Our method can handle general discontinuous cross-correlations among systems with different levels of fidelity. A combination of multi-fidelity Gaussian Processes (AR(1) Co-kriging) and deep neural networks enables us to construct a method that is immune to discontinuities. We demonstrate the effectiveness of the new technology using standard benchmark problems designed to resemble the outputs of complicated high- and low-fidelity codes.