2024/07/01 by Wouter M. Kouw, Kouw, Wouter M., Caspar Gruijthuijsen +7
Decision Sciences · Engineering · #Advanced Control Systems Optimization #Computational Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Fault Detection and Control Systems #Finance #Grey System Theory Applications #Machine Learning (cs.LG) #Systems and Control (eess.SY) #and Science (cs.CE) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2407.01226
openalex publication_date 2024/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a computational procedure for identifying convection in heat transfer dynamics. The procedure is based on a Gaussian process latent force model, consisting of a white-box component (i.e., known physics) for the conduction and linear convection effects and a Gaussian process that acts as a black-box component for the nonlinear convection effects. States are inferred through Bayesian smoothing and we obtain approximate posterior distributions for the kernel covariance function's hyperparameters using Laplace's method. The nonlinear convection function is recovered from the Gaussian process states using a Bayesian regression model. We validate the procedure by simulation error using the identified nonlinear convection function, on both data from a simulated system and measurements from a physical assembly.