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On kernel functions for bi-fidelity Gaussian process regressions | SpringerLink
Source: link.springer.com
Article summary:
1. This paper investigates the impact of kernel functions on the accuracy of bi-fidelity Gaussian process regressions (GPR) for engineering applications.
2. Experiments on four engineering test problems show that the best kernel is problem dependent and sometimes might be counter-intuitive, even when a large amount of low-fidelity data already aids the model.
3. Tuning the regression term is important to improve the accuracy and robustness of bi-fidelity GPR, while decreasing the importance of proper kernel selection.
Article analysis:
The article provides an in-depth analysis of kernel functions for bi-fidelity Gaussian process regressions (GPR) for engineering applications. The authors investigate four different kernels – Gaussian, Matern-3/2, Matern-5/2, and Cubic – as well as their combinations via composite kernel learning (CKL). The experiments conducted on four engineering test problems demonstrate that the best kernel is problem dependent and can be counterintuitive at times. Furthermore, tuning the regression term is important to improve accuracy and robustness of bi-fidelity GPR, while decreasing the importance of proper kernel selection.
The article appears to be reliable and trustworthy overall. It provides a comprehensive overview of its topic with detailed explanations and examples from real world engineering applications. The authors also provide evidence for their claims by citing relevant research papers throughout their discussion. Additionally, they acknowledge potential drawbacks such as expensive computational cost associated with CKL technique which adds to its credibility.
However, there are some points that could have been explored further in order to make this article more comprehensive and balanced in its approach. For instance, it would have been beneficial if the authors had discussed other methods for automatic kernel selection such as Bayesian optimization or genetic algorithms in addition to cross validation and maximum likelihood estimations mentioned in this article. Additionally, it would have been useful if they had provided more information about how tuning regression terms affects accuracy and robustness of bi-fidelity GPRs in detail instead of just mentioning it briefly without any further explanation or evidence supporting this claim.
In conclusion, this article provides a thorough overview of its topic with relevant evidence from real world engineering applications which makes it reliable and trustworthy overall despite some unexplored points that could have made it even more comprehensive and balanced in its approach.