Summary of Gradient-enhanced Deep Gaussian Processes For Multifidelity Modelling, by Viv Bone et al.
Gradient-enhanced deep Gaussian processes for multifidelity modelling
by Viv Bone, Chris van der Heide, Kieran Mackle, Ingo H.J. Jahn, Peter M. Dower, Chris Manzie
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper introduces a novel approach to multifidelity modeling by integrating data from multiple sources using deep Gaussian processes (GPs). The method leverages dense low-fidelity samples to reduce interpolation error and sparse high-fidelity samples to compensate for bias or noise. By incorporating gradient data, the authors demonstrate improved performance on both analytical and realistic problems, including predicting aerodynamic coefficients of a hypersonic flight vehicle. This work extends deep GPs to capture nonlinear relationships between data of different fidelities and outperforms traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper explores a new way to combine information from different sources to create a more accurate model. The approach uses machine learning techniques called deep Gaussian processes, which are good at handling noisy or incomplete data. By adding special information about how the data changes when certain factors are adjusted, the authors can improve their predictions even further. They test this method on two problems: one that’s easy to solve and another that’s more complex, like predicting how a spacecraft will behave in different conditions. |
Keywords
* Artificial intelligence * Machine learning
