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Up to 30% off — ends 7 Aug

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Majdi I Radaideh

University of Michigan

United States

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Research Interests

Computational Neuroscience

Petrochemical Engineering

Genetic Algorithm

Optimization Algorithm

Reactor Physics

Fault Detection

Simulated Annealing

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Articles20

Collaborators3

Xubo GU

National University of Singapore (NUS)

Xingang Zhao

The University of Tennessee Knoxville

UNITED STATES

Brendan Kochunas

University of Michigan

UNITED STATES

How do I reach out?

Meet Kite AI

Majdi I Radaideh

How do I reach out?

Contact this professor

Research Interests

Ask ApplyKite AI

Articles20

Opening the AI black-box: Symbolic regression with Kolmogorov–Arnold Networks for advanced energy applications

A Bayesian ensemble approach for improved sustainable aviation fuel modeling

Efficient sampling of polycyclic aromatic compounds for free energy predictions through active learning

Nuclear microreactor transient and load-following control with deep reinforcement learning

A load following reactivity control system for nuclear microreactors

Fairness and social bias quantification in Large Language Models for sentiment analysis

Data efficiency assessment of generative adversarial networks in energy applications

Multi-objective combinatorial methodology for nuclear reactor site assessment: A case study for the United States

Multistep Criticality Search and Power Shaping in Nuclear Microreactors with Deep Reinforcement Learning

pyMAISE: A Python platform for automatic machine learning and accelerated development for nuclear power applications

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

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

Animorphic ensemble optimization: a large-scale island model

Early Fault Detection in Particle Accelerator Power Electronics Using Ensemble Learning

OpenNeoMC: A framework for design optimization in particle transport simulations based on OpenMC and NEORL

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

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

Model calibration of the liquid mercury spallation target using evolutionary neural networks and sparse polynomial expansions

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

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

Collaborators3

Xubo GU

Xingang Zhao

Brendan Kochunas

How do I reach out?