Romanov, V. (2023). UNet Performance with Wafer Scale Engine (Optimization Case Study). 2023 IEEE High Performance Extreme Computing Conference (HPEC), 1–6. https://doi.org/10.1109/HPEC58863.2023.10363451
Hoover, B., Zaengle, D., Mark-Moser, M., Wingo, P., Suhag, A., and Rose, K., (2023). Enhancing knowledge discovery from unstructured data using a deep learning approach to support subsurface modeling predictions. Frontiers. Big Data 6:1227189. https://doi.org/10.3389/fdata.2023.1227189
Butler, S.K., Barajas-Olalde, C., Yu, X., Mibeck, B.A.F., Burton-Kelly, M.E., Kong, L., Kurz, B., Crandall, D. (2023) Assessing Pore Network Heterogeneity Across Multiple Scales to Inform CO2 Injection Models, International Journal of Greenhouse Gas Control, 130, 104017 https://doi.org/10.1016/j.ijggc.2023.104017
Gromoff, Q., Benzo, P., Saidi, W.A., Andolina, C.M., Casanove, M.J., Hungria, T., Barre, S., Benoit, M., and Lam, J., (2023). Exploring the formation of gold/silver nanoalloys with gas-phase synthesis and machine-learning assisted simulations. Nanoscale, 16(1), 384-393. https://doi.org/10.1039/D3NR04471H
Wan, M., Yang, Z., Morgan, H., Shi, J., Shi, F., Liu, M., Wong, H.W., Gu, Z., and Che, F., (2023). Enhanced CO2 Reactive Capture and Conversion Using Aminothiolate Ligand–Metal Interface. Journal of the American Chemical Society, 145(48), 26038-26051. https://doi.org/10.1021/jacs.3c06888
Johnson, K. B., Ferguson, D., and Nix, A., (2023). Machine-Learning-Based Rotating Detonation Engine Diagnostics: Evaluation for Application in Experimental Facilities. Journal of Propulsion and Power, 1-14. https://doi.org/10.2514/1.B39287
Husain E. Ashkanani, Rui Wang, Wei Shi, Nicholas S. Siefert, Robert L. Thompson, Kathryn H. Smith, Janice A. Steckel, Isaac K. Gamwo, David Hopkinson, Kevin Resnik, Badie I. Morsi, 2023, TEA of the CO2 capture process in pre-combustion applications using thirty-five physical solvents: Predictions with ANN, International Journal of Greenhouse Gas Control, Volume 130, 104007, ISSN 1750-5836. https://doi.org/10.1016/j.ijggc.2023.104007.
Liu, G., Kumar, A., Harbert, W., Siriwardane, H., Crandall, D., Bromhal, G., and L. Cunha. Machine Learning Application for CCUS Carbon Storage: Fracture Analysis and Mapping in the Illinois Basin [Conference Paper]. SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, October 2023. https://doi.org/10.2118/214996-MS
Stinchfield, G., Ammari, B., Morgan, J.C., Siirola, J.D., Zamarripa, M., and C.D. Laird, (2023). Optimization of Process Families for Deployment of Carbon Capture Processes Using Machine Learning Surrogates. Proceedings of the 33rd European Symposium on Computer Aided Process Engineering (ESCAPE33), June 18-21, 2023, Athens, Greece. https://doi.org/10.1016/B978-0-443-15274-0.50212-2
Andolina, C.M., and Saidi, W.A., (2023). Highly transferable atomistic machine-learning potentials from curated and compact datasets across the periodic table. Digital Discovery, 2, 1070-1077. https://doi.org/10.1039/D3DD00046J