Corpus ID: 4469810

Deep Multi-fidelity Gaussian Processes

@article{Raissi2016DeepMG,
  title={Deep Multi-fidelity Gaussian Processes},
  author={Maziar Raissi and George Em Karniadakis},
  journal={ArXiv},
  year={2016},
  volume={abs/1604.07484}
}
  • Maziar Raissi, George Em Karniadakis
  • Published 2016
  • Computer Science, Mathematics
  • 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. [...] Key Method 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.Expand Abstract

    Citations

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    SHOWING 1-10 OF 18 CITATIONS

    Deep Gaussian Processes for Multi-fidelity Modeling

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    CITES METHODS
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    Remarks on multi-output Gaussian process regression

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    CITES BACKGROUND

    Multi-source Deep Gaussian Process Kernel Learning

    VIEW 5 EXCERPTS
    CITES METHODS & BACKGROUND
    HIGHLY INFLUENCED

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    This work was supported by the DARPA project on Scalable Framework for Hierarchical Design and Planning under Uncertainty with Application to Marine Vehicles (N66001-15-2-4055)

    • This work was supported by the DARPA project on Scalable Framework for Hierarchical Design and Planning under Uncertainty with Application to Marine Vehicles (N66001-15-2-4055)