Articles with public access mandates - Majdi I. RadaidehLearn more
Not available anywhere: 2
Thermal Modeling of an eVinci™-like heat pipe microreactor using OpenFOAM
D Price, N Roskoff, MI Radaideh, B Kochunas
Nuclear Engineering and Design 415, 112709, 2023
Mandates: US Department of Energy
OpenNeoMC: A framework for design optimization in particle transport simulations based on OpenMC and NEORL
X Gu, MI Radaideh, J Liang
Annals of Nuclear Energy 180, 109450, 2023
Mandates: National Natural Science Foundation of China
Available somewhere: 27
Neural-based time series forecasting of loss of coolant accidents in nuclear power plants
MI Radaideh, C Pigg, T Kozlowski, Y Deng, A Qu
Expert Systems with Applications 160, 113699, 2020
Mandates: US Department of Energy
Surrogate modeling of advanced computer simulations using deep Gaussian processes
MI Radaideh, T Kozlowski
Reliability Engineering & System Safety 195, 106731, 2020
Mandates: US Department of Energy
Time series anomaly detection in power electronics signals with recurrent and ConvLSTM autoencoders
MI Radaideh, C Pappas, J Walden, D Lu, L Vidyaratne, T Britton, K Rajput, ...
Digital Signal Processing 130, 103704, 2022
Mandates: US Department of Energy
Integrated framework for model assessment and advanced uncertainty quantification of nuclear computer codes under Bayesian statistics
MI Radaideh, K Borowiec, T Kozlowski
Reliability Engineering & System Safety 189, 357-377, 2019
Mandates: US Department of Energy
NEORL: NeuroEvolution Optimization with Reinforcement Learning—Applications to carbon-free energy systems
MI Radaideh, K Du, P Seurin, D Seyler, X Gu, H Wang, K Shirvan
Nuclear Engineering and Design 412, 112423, 2023
Mandates: US Department of Energy
Combining simulations and data with deep learning and uncertainty quantification for advanced energy modeling
MI Radaideh, T Kozlowski
International Journal of Energy Research 43 (14), 7866-7890, 2019
Mandates: US Department of Energy
A new framework for sampling-based uncertainty quantification of the six-group reactor kinetic parameters
MI Radaideh, WA Wieselquist, T Kozlowski
Annals of Nuclear Energy 127, 1-11, 2019
Mandates: US Department of Energy
Advanced BWR criticality safety part I: Model development, model benchmarking, and depletion with uncertainty analysis
MI Radaideh, D Price, D O'Grady, T Kozlowski
Progress in Nuclear Energy 113, 230-246, 2019
Mandates: US Department of Energy
Real electronic signal data from particle accelerator power systems for machine learning anomaly detection
MI Radaideh, C Pappas, S Cousineau
Data in Brief 43, 108473, 2022
Mandates: US Department of Energy
Multiobjective optimization of nuclear microreactor reactivity control system operation with swarm and evolutionary algorithms
D Price, MI Radaideh, B Kochunas
Nuclear Engineering and Design 393, 111776, 2022
Mandates: US Department of Energy
Advanced BWR criticality safety part II: Cask criticality, burnup credit, sensitivity, and uncertainty analyses
D Price, MI Radaideh, D O'Grady, T Kozlowski
Progress in Nuclear Energy 115, 126-139, 2019
Mandates: US Department of Energy
Criticality and uncertainty assessment of assembly misloading in BWR transportation cask
MI Radaideh, D Price, T Kozlowski
Annals of Nuclear Energy 113, 1-14, 2018
Mandates: US Department of Energy
On using computational versus data-driven methods for uncertainty propagation of isotopic uncertainties
MI Radaideh, D Price, T Kozlowski
Nuclear Engineering and Technology 52 (6), 1148-1155, 2020
Mandates: US Department of Energy
Model calibration of the liquid mercury spallation target using evolutionary neural networks and sparse polynomial expansions
MI Radaideh, H Tran, L Lin, H Jiang, D Winder, S Gorti, G Zhang, J Mach, ...
Nuclear Instruments and Methods in Physics Research Section B: Beam …, 2022
Mandates: US Department of Energy
Bayesian inverse uncertainty quantification of the physical model parameters for the spallation neutron source first target station
MI Radaideh, L Lin, H Jiang, S Cousineau
Results in Physics 36, 105414, 2022
Mandates: US Department of Energy
Early fault detection in particle accelerator power electronics using ensemble learning
MI Radaideh, C Pappas, M Wezensky, P Ramuhalli, S Cousineau
International Journal of Prognostics and Health Management 14 (1), 2023
Mandates: US Department of Energy
Application of convolutional and feedforward neural networks for fault detection in particle accelerator power systems
M Radaideh, C Pappas, P Ramuhalli, S Cousineau
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States) 14 (1), 2022
Mandates: US Department of Energy
Multi-module-based CVAE to predict HVCM faults in the SNS accelerator
Y Alanazi, M Schram, K Rajput, S Goldenberg, L Vidyaratne, C Pappas, ...
Machine Learning with Applications 13, 100484, 2023
Mandates: US Department of Energy
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