A Random Fourier Feature Method for Emulating Computer Models With Gradient Information. Issue 4 (2nd October 2021)

• Record Type: Journal Article
• Authors: Hung, Tzu-Hsiang; Chien, Peter
• Is Part Of: Technometrics. Volume 63:Issue 4(2021)
• Page Start: 500
• Page End: 509
• Publication Date: 2021-10-02
• Subjects: Computer models -- Gradient-enhanced Gaussian process emulator -- Random Fourier features; Statistical physics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; Engineering -- Statistical methods -- Periodicals; 519.5
• DOI: 10.1080/00401706.2020.1852973
• Languages: English
• ISSNs: 0040-1706

Abstract

Abstract: Computer models with gradient information are increasingly used in engineering and science. The gradient-enhanced Gaussian process emulator can be used for emulating such models. Because the size of the covariance matrix increases proportionally with the dimension of inputs and the sample size, it is computationally challenging to fit such an emulator for large datasets. We propose a random Fourier feature method to mitigate this difficulty. The key idea of the proposed method is to employ random Fourier features to obtain an easily computable, low-dimensional feature representation for shift-invariant kernels involving gradients. The effectiveness of the proposed method is illustrated by several examples.