@article{8902,
  author = {R.J. R. van Kampen and M. van Berkel and H. Zwart},
  title = {Estimating Space-Dependent Coefficients for 1D Transport Using Gaussian Processes as State Estimator in the Frequency Domain},
  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.
},
  year = {2023},
  journal = {IEEE Control Systems Letters},
  volume = {7},
  pages = {247-252},
  month = {06/2022},
  doi = {10.1109/LCSYS.2022.3186626},
  language = {eng},
}