Abstract In internal combustion engines, access to calibrated physical models of engine subsystems is crucial for building predictive models of engine emissions. Specifically, understanding the temperature and pressure dynamics of the intake manifold (IM) can help characterize the behavior of the cylinder contents, which largely impact the performance. Rather than working with a complex and multidimensional model, we can employ a mean value model (MVM) to accurately capture the IM states. However, in situations where heat transfer affects cannot be ignored, the standard MVM contains unknown physical parameters representing various material properties. This also causes the dynamics to appear in a nonlinear way. When coupled with noisy measurement data, a Bayesian approach is often necessary to reconstruct both the IM states and physical parameters. One would typically apply a variant of the Kalman filter suitable for nonlinear dynamics such as particle filters. For real engine datasets this is computationally unfeasible, as there are often hundreds of thousands of data points sampled in a matter of minutes. In this work, we demonstrate how information field theory (IFT) can be applied to solve the IM filtering and calibration problem. IFT is a scientific machine learning framework for simultaneous Bayesian calibration of dynamic states and physical parameters. We demonstrate the method across different datasets with varying degrees of fidelity collected from real engines. By informing the IFT prior of the MVM, the model predictions remain robust even with limited data. Finally, the IFT posterior also quantifies the uncertainty about the IM material properties.
Alberts et al. (2026) studied this question.