Majdi Radaideh
Applications:
Complex Systems, Engineering
Methodologies:
Bayesian Methods, Machine Learning, Natural Language Processing, Optimization

Majdi Radaideh

Assistant Professor

Nuclear Engineering and Radiological Sciences

Prof. Majdi Radaideh leads the Artificial Intelligence and Multiphysics Simulations lab (AIMS), which focuses on the intersection between nuclear reactor design, nuclear multiphysics modeling and simulation, advanced computational methods, and machine learning algorithms to drive advanced reactor research and improve the sustainability of the current reactor fleet. AIMS extensively employs data science and machine learning methods for various goals including but not limited to:
1- Development of surrogate models for expensive nuclear reactor simulations in steady-state and time-dependent modes using convolutional and recurrent neural networks.
2- Large-scale combinatorial optimization to improve the performance of the nuclear fuel inside nuclear power plants using physics-informed reinforcement learning and neuroevolution algorithms.
3- Long-short term memory and ensemble methods for anomaly detection and fault prognosis to monitor the health of the nuclear power plant components.
4- Uncertainty quantification of data-driven models with Bayesian inference and Gaussian processes.
5- Natural language processing methods to process nuclear plant maintenance and burnup records.

AIMS lab aims on bridging the gap between nuclear reactor design, nuclear multiphysics modeling and simulation, advanced computational methods, and machine learning algorithms to drive advanced nuclear reactor research and improve the sustainability of the current reactor fleet to promote nuclear power as a carbon-free energy source in order to achieve net-zero carbon emission.