Grigorios Delipei

Development and application of two-step uncertainty propagation and sensitivity analysis methodology for fast reactor safety analysis

Trivedi, I., Delipei, G., Hou, J., Grasso, G., & Ivanov, K. (2025, February 11). Nuclear Engineering and Design, Vol. 433.

High-resolution predictions of the coolant properties for the 3D PWR core with artificial neural networks based on CTF

Papadionysiou, M., Delipei, G., Avramova, M., Ferroukhi, H., & Ivanov, K. (2025, July 1). Nuclear Engineering and Design, Vol. 442.

Uncertainty Quantification Framework for High-Temperature Gas-Cooled Reactors Using VSOP and DAKOTA

Delipei, G. K., Altahhan, M., Rouxelin, P., Sen, S., George, N., Avramova, M., & Ivanov, K. (2025, May 13). Nuclear Science and Engineering, Vol. 5.

Uncertainty Quantification for Data-Driven Machine Learning Models in Nuclear Engineering Applications: Where We Are and What Do We Need?

Wu, X., Moloko, L. E., Bokov, P. M., Delipei, G. K., Kaizer, J., & Ivanov, K. N. (2025, October 22). Nuclear Science and Engineering.

A Framework for Multi-Physics Modeling, Design Optimization and Uncertainty Quantification of Fast-Spectrum Liquid-Fueled Molten-Salt Reactors

Holler, D., Bhaskar, S., Delipei, G., Avramova, M., & Ivanov, K. (2024, August 28). Applied Sciences, Vol. 14.

A component diagnostic and prognostic framework for pump bearings based on deep learning with data augmentation

Rivas, A., Delipei, G. K., Davis, I., Bhongale, S., Yang, J., & Hou, J. (2024, April 3). Reliability Engineering & System Safety, Vol. 247.

A system diagnostic and prognostic framework based on deep learning for advanced reactors

Rivas, A., Delipei, G. K., Davis, I., Bhongale, S., & Hou, J. (2024, February 13). Progress in Nuclear Energy, Vol. 170.

Fuel performance code to code comparative analysis for the OECD/NEA MPCMIV benchmark

Faure, Q., Delipei, G., Scolaro, A., Avramova, M., & Ivanov, K. (2024, November 13). Nuclear Engineering and Design, Vol. 430.

International fuel performance study of fresh fuel experiments for PCMI effects during RIA experiments

Seo, S., Folsom, C., Jensen, C., Kamerman, D., Giaccardi, L., Cherubini, M., … Fiorina, C. (2024, November 2). Nuclear Engineering and Design.

Modeling a generic Pebble Bed High-Temperature Gas-Cooled Reactor to perform load-following using Simulink

Rivas, A., Delipei, G. K., & Hou, J. (2024, August 5). Nuclear Engineering and Design, Vol. 428.

Operation Optimization Framework for Advanced Reactors Using a Data-Driven Digital Twin

Journal of Nuclear Engineering and Radiation Science.

Reinforcement Learning-Based Control Sequence Optimization for Advanced Reactors

Nguyen, K. H. N., Rivas, A., Delipei, G. K., & Hou, J. (2024, July 1). Journal of Nuclear Engineering, Vol. 5, pp. 209–225.

Fuel performance uncertainty quantification and sensitivity analysis in the presence of epistemic and aleatoric sources of uncertainties

Frontiers in Energy Research, 11.

CTF-PARCS Core Multi-Physics Computational Framework for Efficient LWR Steady-State, Depletion and Transient Uncertainty Quantification

Energies, 15(14), 5226.

Predictions of component Remaining Useful Lifetime Using Bayesian Neural Network

Rivas, A., Delipei, G. K., & Hou, J. (2022, February 21). Progress in Nuclear Energy, Vol. 146, p. 104143.

Summary of comparative analysis and conclusions from OECD/NEA LWR-UAM benchmark Phase I

Nuclear Engineering and Design, 384, 111474.