Advanced stationary and nonstationary kernel designs for domain-aware Gaussian processes

DOI10.2140/CAMCOS.2022.17.131zbMath1498.60012arXiv2102.03432OpenAlexW3126744387MaRDI QIDQ2095243

Marcus M. Noack, James A. Sethian

Publication date: 9 November 2022

Published in: Communications in Applied Mathematics and Computational Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2102.03432

zbMATH Keywords

Gaussian processesmachine learninguncertainty quantification

Mathematics Subject Classification ID

Gaussian processes (60G15) Computational methods for problems pertaining to probability theory (60-08)

Cites Work

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• Hybrid genetic deflated Newton method for global optimisation
• Estimating Shape Constrained Functions Using Gaussian Processes
• Regression‐based covariance functions for nonstationary spatial modeling

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