  | PAPAVASILEIOU, P., Giovanis, D. G., POZZETTI, G., Kathrein, M., Czettl, C., Kevrekidis, I. G., Boudouvis, A. G., BORDAS, S., & KORONAKI, E. (January 2025). Integrating supervised and unsupervised learning approaches to unveil critical process inputs. Computers and Chemical Engineering, 192, 108857. doi:10.1016/j.compchemeng.2024.108857  Peer reviewed |
  | LOACHAMIN SUNTAXI, G., PAPAVASILEIOU, P., KORONAKI, E., Giovanis, D. G., Gakis, G., Aviziotis, I. G., Kathrein, M., POZZETTI, G., Czettl, C., Bordas, S. P. A., & Boudouvis, A. G. (15 November 2024). Discovering deposition process regimes: Leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis. Chemical Engineering Journal Advances, 20, 100667. doi:10.1016/j.ceja.2024.100667 Peer reviewed |
 | KORONAKI, E., Kaven, L. F., Faust, J. M. M., Kevrekidis, I. G., & Mitsos, A. (October 2024). Nonlinear manifold learning determines microgel size from Raman spectroscopy. AIChE Journal, 70 (10). doi:10.1002/aic.18494 Peer Reviewed verified by ORBi |
 | PAPAVASILEIOU, P., FARINA, S., KORONAKI, E., BOUDOUVIS, A., BORDAS, S., & SKUPIN, A. (2024). Machine Learning-based Predictions of Spatial Metabolic Profiles Demonstrate the Impact of Morphology on Astrocytic Energy Metabolism. (preprint). ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/62074. doi:10.1101/2024.09.18.613725 |
 | KORONAKI, E., Evangelou, N., Martin-Linares, C. P., Titi, E. S., & Kevrekidis, I. G. (June 2024). Nonlinear dimensionality reduction then and now: AIMs for dissipative PDEs in the ML era. Journal of Computational Physics, 506, 112910. doi:10.1016/j.jcp.2024.112910 Peer Reviewed verified by ORBi |
 | Martin-Linares, C. P., Psarellis, Y. M., Karapetsas, G., KORONAKI, E., & Kevrekidis, I. G. (27 November 2023). Physics-agnostic and physics-infused machine learning for thin films flows: Modelling, and predictions from small data. Journal of Fluid Mechanics, 975. doi:10.1017/jfm.2023.868 Peer Reviewed verified by ORBi |
 | KORONAKI, E., Evangelou, N., Psarellis, Y. M., Boudouvis, A. G., & Kevrekidis, I. G. (October 2023). From partial data to out-of-sample parameter and observation estimation with diffusion maps and geometric harmonics. Computers and Chemical Engineering, 178, 108357. doi:10.1016/j.compchemeng.2023.108357 Peer Reviewed verified by ORBi |
 | PAPAVASILEIOU, P., KORONAKI, E., POZZETTI, G., Kathrein, M., Czettl, C., Boudouvis, A. G., & BORDAS, S. (August 2023). Equation-based and data-driven modeling strategies for industrial coating processes. Computers in Industry, 149, 103938. doi:10.1016/j.compind.2023.103938 Peer Reviewed verified by ORBi |
 | PAPAVASILEIOU, P., KORONAKI, E., POZZETTI, G., Kathrein, M., Czettl, C., Boudouvis, A. G., Mountziaris, T. J., & BORDAS, S. (October 2022). An efficient chemistry-enhanced CFD model for the investigation of the rate-limiting mechanisms in industrial Chemical Vapor Deposition reactors. Chemical Engineering Research and Design, 186, 314 - 325. doi:10.1016/j.cherd.2022.08.005 Peer Reviewed verified by ORBi |
 | Spencer, R., Gkinis, P., KORONAKI, E., Gerogiorgis, D. I., BORDAS, S., & Boudouvis, A. G. (June 2021). Investigation of the chemical vapor deposition of Cu from copper amidinate through data driven efficient CFD modelling. Computers and Chemical Engineering, 149, 107289. doi:10.1016/j.compchemeng.2021.107289 Peer Reviewed verified by ORBi |
