Bayesian approach

A machine learning approach for determining temperature-dependent bandgap of metal oxides utilizing Allen–Heine–Cardona theory and O’Donnell model parameterization

Nandi, T., Chong, L., Park, J., Saidi, W.A., Chorpening, B., Bayham, S., and Duan, Y. (2024) A machine learning approach for determining temperature-dependent bandgap of metal oxides utilizing Allen–Heine–Cardona theory and O’Donnell model parameterization. AIP Advances, 14, 035231. https://doi.org/10.1063/5.0190024

Kinetic Model Development and Bayesian Uncertainty Quantification for the Complete Reduction of Fe-based Oxygen Carriers with CH4, CO, and H2 for Chemical Looping Combustion

Ostace, A., Chen, Y.Y., Parker, R., Mebane, D.S., Okoli, C., Lee, A., Tong, A., Fan, L.S., Biegler, L.T., Burgard, A.P., Miller, D.C., & Bhattacharyya, D. (2021). Kinetic Model Development and Bayesian Uncertainty Quantification for the Complete Reduction of Fe-based Oxygen Carriers with CH4, CO, and H2 for Chemical Looping Combustion. Chemical Engineering Science, 252 (28), https://doi.org/10.1016/j.ces.2022.117512