Sensitivity-guided iterative parameter identification and data generation with BayesFlow and PELS-VAE for model calibration

Abstract

Calibration of complex system models with a large number of parameters using standard optimization methods is often extremely time-consuming and not fully automated due to the reliance on all-inclusive expert knowledge. We propose a sensitivity-guided iterative parameter identification and data generation algorithm. The sensitivity analysis replaces manual intervention, the parameter identification is realized by BayesFlow allowing for uncertainty quantification, and the data generation with the physics-enhanced latent space variational autoencoder (PELS-VAE) between two iteration steps enables inference of weakly identifiable parameters. A complete calibration experiment was conducted on a thermal model of an automotive cabin. The average relative error rate over all parameter estimates of 1.62% and the mean absolute error of calibrated model outputs of $$0.108\,^{\circ }$$ 0.108 ∘ C validate the feasibility and effectiveness of the method. Moreover, the entire procedure accelerates up to 1 day, whereas the classical calibration method takes more than 1 week.