|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|
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.
|Alternate Title||IEEE Control Syst. Lett.|
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