TY - JOUR A1 - Bubel, Martin A1 - Schmid, Jochen A1 - Carmesin, Maximilian A1 - Kozachynskyi, Volodymyr A1 - Esche, Erik A1 - Bortz, Michael T1 - Cubature-based uncertainty estimation for nonlinear regression models N2 - Models are commonly utilized in chemical engineering to simulate real-world processes and phenomena. Given their role in guiding decision-making, accurately quantifying the uncertainty of these models is essential. Typically, these models are calibrated using experimental data that contain measurement errors, leading to uncertainty in the fitted model parameters. Current methods for estimating the prediction uncertainty of nonlinear regression models are often either computationally intensive or biased. In this study, we use sparse cubature formulas to estimate the prediction uncertainty of nonlinear regression models. Our findings indicate that this method provides a favorable balance between accuracy and computational efficiency, making it suitable for application in chemical engineering. We validate the performance of our proposed method through various regression case studies, including both theoretical toy models and practical models from chemical engineering. KW - Nonlinear models KW - Model uncertainty KW - Parameter estimation PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-628008 DO - https://doi.org/10.1016/j.compchemeng.2025.109035 SN - 1873-4375 VL - 197 SP - 1 EP - 23 PB - Elsevier Ltd. AN - OPUS4-62800 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -