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

Ricky J.R. van Kampen (Corresponding author), Matthijs van Berkel, Hans Zwart

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
104 Downloads (Pure)

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.
Original languageEnglish
Article number9807334
Pages (from-to)247-252
Number of pages6
JournalIEEE Control Systems Letters
Volume7
DOIs
Publication statusPublished - 2023

Keywords

  • Measurement uncertainty
  • Gaussian processes
  • Standards
  • Mathematical models
  • Kernel
  • Uncertainty
  • State estimation
  • Grey-box modeling
  • Inverse problems
  • Identification
  • Distributed parameter systems
  • grey-box modeling
  • identification
  • inverse problems
  • distributed parameter systems

Fingerprint

Dive into the research topics of 'Estimating Space-Dependent Coefficients for 1D Transport Using Gaussian Processes as State Estimator in the Frequency Domain'. Together they form a unique fingerprint.

Cite this