Gaussian Process Regression for Data Fulfilling Linear Differential Equations with Localized Sources

Gaussian Process Regression for Data Fulfilling Linear Differential Equations with Localized Sources

Specialized Gaussian process regression is presented for data that are known to fulfill a given linear differential equation with vanishing or localized sources. The method allows estimation of system parameters as well as strength and location of point sources. It is applicable to a wide range of d...

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Bibliographic Details
Main Authors: Christopher G. Albert, Katharina Rath
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Entropy
Subjects:
gaussian process regression
physics-informed methods
kernel methods
field reconstruction
source localization
partial differential equations
meshless methods
Online Access:https://www.mdpi.com/1099-4300/22/2/152

Internet

https://www.mdpi.com/1099-4300/22/2/152

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