Optimization of hyperparameters of Gaussian process regression with the help of a low-order high-dimensional model representation: application to a potential energy surface

DOI10.1007/s10910-022-01407-xOpenAlexW4300089463MaRDI QIDQ2679251

Manabu Ihara, Sergei Manzhos

Publication date: 19 January 2023

Published in: Journal of Mathematical Chemistry (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10910-022-01407-x

zbMATH Keywords

potential energy surfaceGaussian process regressionhigh-dimensional model representationhyperparameter optimizationscarce data

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Linear regression; mixed models (62J05) General nonlinear regression (62J02) Molecular physics (81V55)

Cites Work

• Tutorial on maximum likelihood estimation
• General foundations of high-dimensional model representations
• Inverse multiquadratic functions as the basis for the rectangular collocation method to solve the vibrational Schrödinger equation
• Gaussian processes for time-series modelling
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