Bayesian inference for estimating model discrepancy of an electric motor

Uncertainty Quantification (UQ) is highly requested in computational modeling and simulation, especially in an industrial context. A main challenge is related to the fact that computational models are rarely able to represent the true physics perfectly and demonstrate a discrepancy compared to measurement data. Further, an accurate knowledge of considered model parameters is usually not available. E.g. fluctuations in manufacturing processes of hardware components introduce uncertainties which must be quantified in an appropriate way. Mathematically, such UQ tasks are posed as inverse problems, requiring efficient methods for their solution. We address this challenge by investigating the influence of model discrepancies onto the calibration of model parameters and further consider a Bayesian inference framework including an attempt to correct for model discrepancy by an additional term. A Markov Chain Monte Carlo (MCMC) method is utilized to approximate the posterior distribution. Synthetic measurement data from an electric motor model and an artificial model error are used to evaluate the framework. The application shows a promising perspective of the framework by good approximation of discrepancy and parameters.