Estimating Space-Dependent Coefficients for 1D Transport Using Gaussian Processes as State Estimator in the Frequency Domain

TitleEstimating Space-Dependent Coefficients for 1D Transport Using Gaussian Processes as State Estimator in the Frequency Domain
Publication TypeJournal Article
Year of Publication2023
AuthorsR.J.R van Kampen, M. van Berkel, H. Zwart
JournalIEEE Control Systems Letters
Volume7
Pagination247-252
Date Published06/2022
Abstract

This letter presents a method to estimate the space-dependent transport coefficients (diffusion, convection, reaction, and source/sink) for a generic scalar transport model, e.g., heat or mass. As the problem is solved in the frequency domain, the complex valued state as a function of the spatial variable is estimated using Gaussian process regression. The resulting probability density function of the state, together with a semi-discretization of the model, and a linear parameterization of the coefficients are used to determine the maximum likelihood solution for these space-dependent coefficients. The proposed method is illustrated by simulations.

DOI10.1109/LCSYS.2022.3186626
Division

FP

Department

ESC

PID50de0e394684a6246fc7d9fa81290c85
Alternate TitleIEEE Control Syst. Lett.
LabelOA
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