TY - GEN
T1 - A constrained semi-parametric gaussian process using bayesian-entropy regression
AU - Wang, Yuhao
AU - Gao, Yi
AU - Liu, Yongming
AU - Ghosh, Sayan
AU - Wang, Liping
N1 - Funding Information:
The research reported in this paper was supported by funds from NASA University Leadership Initiative program (Contract No. NNX17AJ86A, Project Officer: Dr. Anupa Bajwa, Principal Investigator: Dr. Yongming Liu). The support is gratefully acknowledged.
Publisher Copyright:
© 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2021
Y1 - 2021
N2 - A Gaussian Process makes prediction based on the existing observed data. But in many cases, information is not limited to observations. Extra information, such as physical constraints and empirical knowledge, exists in many engineering problems. This paper presents a Bayesian-Entropy method to encode constraints into a Semiparametric Gaussian Process. The Bayesian-Entropy method can encode various types of constraints by adding an additional term to the Bayesian equation. The Bayesian-Entropy regression method can incorporate values and derivative information into the classical Bayesian regression as constraint. By adjusting the mean function in Semiparametric Gaussian Process according to the Bayesian-Entropy regression principle, extra information, such as the expected value and/or the derivative at a specific point, can be encoded into the regression function. Comparing with the traditional method, the constrained Semiparametric Gaussian Process benefits from the available extra information and can make better prediction outside the range of training data.
AB - A Gaussian Process makes prediction based on the existing observed data. But in many cases, information is not limited to observations. Extra information, such as physical constraints and empirical knowledge, exists in many engineering problems. This paper presents a Bayesian-Entropy method to encode constraints into a Semiparametric Gaussian Process. The Bayesian-Entropy method can encode various types of constraints by adding an additional term to the Bayesian equation. The Bayesian-Entropy regression method can incorporate values and derivative information into the classical Bayesian regression as constraint. By adjusting the mean function in Semiparametric Gaussian Process according to the Bayesian-Entropy regression principle, extra information, such as the expected value and/or the derivative at a specific point, can be encoded into the regression function. Comparing with the traditional method, the constrained Semiparametric Gaussian Process benefits from the available extra information and can make better prediction outside the range of training data.
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M3 - Conference contribution
AN - SCOPUS:85100404102
SN - 9781624106095
T3 - AIAA Scitech 2021 Forum
SP - 1
EP - 9
BT - AIAA Scitech 2021 Forum
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021
Y2 - 11 January 2021 through 15 January 2021
ER -