[2511.18178] Bayesian Calibration of Engine-out NOx Models for Engine-to-Engine Transferability

Computer Science > Machine Learning arXiv:2511.18178 (cs) [Submitted on 22 Nov 2025 (v1), last revised 25 Mar 2026 (this version, v2)] Title:Bayesian Calibration of Engine-out NOx Models for Engine-to-Engine Transferability Authors:Shrenik Zinage, Peter Meckl, Ilias Bilionis View a PDF of the paper titled Bayesian Calibration of Engine-out NOx Models for Engine-to-Engine Transferability, by Shrenik Zinage and 2 other authors View PDF HTML (experimental) Abstract:Accurate prediction of engine-out NOx is essential for meeting stringent emissions regulations and optimizing engine performance. Traditional approaches rely on models trained on data from a small number of engines, which can be insufficient in generalizing across an entire population of engines due to sensor biases and variations in input conditions. In real world applications, these models require tuning or calibration to maintain acceptable error tolerance when applied to other engines. This highlights the need for models that can adapt with minimal adjustments to accommodate engine-to-engine variability and sensor discrepancies. While previous studies have explored machine learning methods for predicting engine-out NOx, these approaches often fail to generalize reliably across different engines and operating environments. To address these issues, we propose a Bayesian calibration framework that combines Gaussian processes (GP) with approximate Bayesian computation to infer and correct sensor biases. Starting wi...