M. Radaideh

发表

Physics-informed reinforcement learning optimization of nuclear assembly design

N. Roy, J. Joseph, B. Forget, 2020, Nuclear Engineering and Design.

Shapley effect application for variance-based sensitivity analysis of the few-group cross-sections

M. Radaideh, Stuti Surani, D. O'grady, 2019, Annals of Nuclear Energy.

Efficient analysis of parametric sensitivity and uncertainty of fuel cell models with application to SOFC

Mohammed I. Radaideh, M. Radaideh, Mohammad I. Radaideh, 2019, International Journal of Energy Research.

Multiobjective optimization of nuclear microreactor reactivity control system operation with swarm and evolutionary algorithms

B. Kochunas, M. Radaideh, D. Price, 2022, Nuclear Engineering and Design.

Advanced BWR criticality safety part I: Model development, model benchmarking, and depletion with uncertainty analysis

M. Radaideh, D. O'grady, T. Kozlowski, 2019, Progress in Nuclear Energy.

ON THE EFFECT OF FINE-SCALE RADIAL VOID DISTRIBUTIONS ON ASSEMBLY CRITICALITY AND PIN POWER DISTRIBUTION

M. Radaideh, T. Kozlowski, D. Price, 2021, EPJ Web of Conferences.

PESA: Prioritized experience replay for parallel hybrid evolutionary and swarm algorithms - Application to nuclear fuel

M. Radaideh, K. Shirvan, 2022, Nuclear Engineering and Technology.

Time series anomaly detection in power electronics signals with recurrent and ConvLSTM autoencoders

M. Schram, M. Radaideh, L. Vidyaratne, 2022, Digit. Signal Process..

Animorphic ensemble optimization: a large-scale island model

M. Radaideh, D. Price, 2022, Neural Computing and Applications.

Data-Driven and Precursor-Group Uncertainty Propagation of Lattice Kinetic Parameters in UAM Benchmark

M. Radaideh, M. Jessee, T. Kozlowski, 2019, Science and Technology of Nuclear Installations.

Application of Convolutional and Feedforward Neural Networks for Fault Detection in Particle Accelerator Power Systems

M. Radaideh, P. Ramuhalli, C. Pappas, 2022, Annual Conference of the PHM Society.

NEORL: NeuroEvolution Optimization with Reinforcement Learning pdf

Majdi I. Radaideh, Koroush Shirvan, Katelin Du, 2021, ArXiv.

Surrogate modeling of advanced computer simulations using deep Gaussian processes

Tomasz Kozlowski, Majdi I. Radaideh, M. Radaideh, 2020, Reliab. Eng. Syst. Saf..

Analyzing nuclear reactor simulation data and uncertainty with the group method of data handling

Majdi I. Radaideh, Tomasz Kozlowski, M. Radaideh, 2020 .

Bayesian Inverse Uncertainty Quantification of the Physical Model Parameters for the Spallation Neutron Source First Target Station

Hao Jiang, M. Radaideh, Lianshan Lin, 2022, SSRN Electronic Journal.

Multiphysics Modeling and Validation of Spent Fuel Isotopics Using Coupled Neutronics/Thermal-Hydraulics Simulations

M. Radaideh, T. Kozlowski, D. Price, 2020 .

Multi-objective Optimization of Nuclear Microreactor Control System Operation with Swarm and Evolutionary Algorithms

B. Kochunas, M. Radaideh, D. Price, 2022 .

Rule-based reinforcement learning methodology to inform evolutionary algorithms for constrained optimization of engineering applications

Majdi I. Radaideh, Koroush Shirvan, M. Radaideh, 2021, Knowl. Based Syst..

Combining simulations and data with deep learning and uncertainty quantification for advanced energy modeling

Tomasz Kozlowski, Majdi I. Radaideh, M. Radaideh, 2019, International Journal of Energy Research.

Neural-based time series forecasting of loss of coolant accidents in nuclear power plants

Tomasz Kozlowski, Majdi I. Radaideh, Connor Pigg, 2020, Expert Syst. Appl..

A new framework for sampling-based uncertainty quantification of the six-group reactor kinetic parameters

M. Radaideh, T. Kozlowski, W. Wieselquist, 2019, Annals of Nuclear Energy.

Design optimization under uncertainty of hybrid fuel cell energy systems for power generation and cooling purposes

Tomasz Kozlowski, Majdi I. Radaideh, Mohammed I. Radaideh, 2020 .

Large-scale design optimisation of boiling water reactor bundles with neuroevolution

Benoit Forget, Majdi I. Radaideh, Koroush Shirvan, 2021 .

Determination and validation of photon energy absorption buildup factor in human tissues using Monte Carlo simulation

Rizwan-uddin, M. Radaideh, T. Kozlowski, 2019, Radiation Physics and Chemistry.

Reactivity and flux characterization of the Jordan subcritical assembly

M. Radaideh, S. Malkawi, I. Jarrah, 2018, Progress in Nuclear Energy.

Experimental validation of the neutronic parameters in the Jordan subcritical assembly

M. Radaideh, S. Malkawi, I. Jarrah, 2018, Progress in Nuclear Energy.

Early Fault Detection in Particle Accelerator Power Electronics Using Ensemble Learning

M. Radaideh, P. Ramuhalli, C. Pappas, 2023, International Journal of Prognostics and Health Management.

Multi-module based CVAE to predict HVCM faults in the SNS accelerator

M. Schram, M. Radaideh, P. Ramuhalli, 2023, Machine Learning with Applications.

Fault Prognosis in Particle Accelerator Power Electronics Using Ensemble Learning

M. Radaideh, P. Ramuhalli, C. Pappas, 2022, ArXiv.

Real electronic signal data from particle accelerator power systems for machine learning anomaly detection

M. Radaideh, C. Pappas, Sarah Cousineau, 2022, Data in brief.

Model Calibration of the Liquid Mercury Spallation Target using Evolutionary Neural Networks and Sparse Polynomial Expansions

Hao Jiang, M. Radaideh, Guannan Zhang, 2022, Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms.

Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions

M. Schram, M. Radaideh, T. Britton, 2023, ArXiv.

Criticality and uncertainty assessment of assembly misloading in BWR transportation cask

M. Radaideh, D. Price, T. Kozłowski, 2018 .

NEORL: NeuroEvolution Optimization with Reinforcement Learning—Applications to carbon-free energy systems

M. Radaideh, K. Shirvan, Paul Seurin, 2023, Nuclear Engineering and Design.

A Comparative Analysis of Text-to-Image Generative AI Models in Scientific Contexts: A Case Study on Nuclear Power

M. Radaideh, Veda Joynt, Jacob Cooper, 2023, ArXiv.