Deep Multi-fidelity Gaussian Processes
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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.
Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Apr 2016.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 3; 2024: 2; 2025: 3; 2026: 2.
15 papers.
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- 2021Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantitiescited by 31
- 2022Multi-fidelity surrogate modeling using long short-term memory networkscited by 18
- 2022Multi-fidelity Hierarchical Neural Processescited by 15
- 2024Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modelingcited by 13
- 2023Disentangled Multi-Fidelity Deep Bayesian Active Learningcited by 11
- 2022Deep convolutional neural network for shape optimization using level-set approachcited by 1
- 2023Open Problems in Applied Deep Learningcited by 1
- 2026Multi-Fidelity SINDy: Sparse Discovery of Nonlinear Dynamical Systems with Fidelity-Weighted Measurementscited by 1
- 2021Gradient-enhanced multifidelity neural networks for high-dimensional function approximationcited by 0
- 2023Physics-informed reinforcement learning via probabilistic co-adjustment functionscited by 0
- 2024Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regressioncited by 0
- 2025On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiological boundary conditionscited by 0
- 2026Multi-Fidelity SINDy: Sparse Discovery of Nonlinear Dynamical Systems with Fidelity-Weighted Measurements14 Jun 2026
- 2026Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data23 Mar 2026
- 2025A Constrained Multi-Fidelity Bayesian Optimization Method13 Oct 2025
- 2025Multi-modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation26 Sep 2025
- 2025On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiological boundary conditions13 Jun 2025
- 2024Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression14 Mar 2024
- 2024Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling29 Feb 2024
- 2023Physics-informed reinforcement learning via probabilistic co-adjustment functions11 Sep 2023
- 2023Disentangled Multi-Fidelity Deep Bayesian Active Learning7 May 2023
- 2023Open Problems in Applied Deep Learning26 Jan 2023
- 2022Multi-fidelity surrogate modeling using long short-term memory networks5 Aug 2022
- 2022Multi-fidelity Hierarchical Neural Processes10 Jun 2022
3 more
- 2022Deep convolutional neural network for shape optimization using level-set approach17 Jan 2022
- 2021Gradient-enhanced multifidelity neural networks for high-dimensional function approximation23 Mar 2021
- 2021Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities26 Feb 2021
- 2021Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantitiescited by 31
- 2022Multi-fidelity surrogate modeling using long short-term memory networkscited by 18
- 2022Multi-fidelity Hierarchical Neural Processescited by 15
- 2024Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modelingcited by 13
- 2023Disentangled Multi-Fidelity Deep Bayesian Active Learningcited by 11
- 2022Deep convolutional neural network for shape optimization using level-set approachcited by 1
- 2023Open Problems in Applied Deep Learningcited by 1
- 2026Multi-Fidelity SINDy: Sparse Discovery of Nonlinear Dynamical Systems with Fidelity-Weighted Measurementscited by 1
- 2021Gradient-enhanced multifidelity neural networks for high-dimensional function approximationcited by 0
- 2023Physics-informed reinforcement learning via probabilistic co-adjustment functionscited by 0
- 2024Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regressioncited by 0
- 2025On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiological boundary conditionscited by 0
12 most cited of 15 All 15 (JSON)
- 2026Multi-Fidelity SINDy: Sparse Discovery of Nonlinear Dynamical Systems with Fidelity-Weighted Measurements14 Jun 2026
- 2026Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data23 Mar 2026
- 2025A Constrained Multi-Fidelity Bayesian Optimization Method13 Oct 2025
- 2025Multi-modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation26 Sep 2025
- 2025On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiological boundary conditions13 Jun 2025
- 2024Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression14 Mar 2024
- 2024Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling29 Feb 2024
- 2023Physics-informed reinforcement learning via probabilistic co-adjustment functions11 Sep 2023
- 2023Disentangled Multi-Fidelity Deep Bayesian Active Learning7 May 2023
- 2023Open Problems in Applied Deep Learning26 Jan 2023
- 2022Multi-fidelity surrogate modeling using long short-term memory networks5 Aug 2022
- 2022Multi-fidelity Hierarchical Neural Processes10 Jun 2022
12 newest of 15 All 15 (JSON)