Title | Estimating Space-Dependent Coefficients for 1D Transport Using Gaussian Processes as State Estimator in the Frequency Domain |
Publication Type | Journal Article |
Year of Publication | 2023 |
Authors | R.J.R van Kampen, M. van Berkel, H. Zwart |
Journal | IEEE Control Systems Letters |
Volume | 7 |
Pagination | 247-252 |
Date Published | 06/2022 |
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. |
DOI | 10.1109/LCSYS.2022.3186626 |
Division | FP |
Department | ESC |
PID | 50de0e394684a6246fc7d9fa81290c85 |
Alternate Title | IEEE Control Syst. Lett. |
Label | OA |
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