My research focus is on the fusion of Artificial Intelligence and Control theory to design and develop autonomous control systems capable of decision-making, prognosis, diagnosis, adaptation, and control. My research goal in this fellowship is to enhance AI/ML capabilities for control theory with applications to complex engineering systems like nuclear power plants (NPPs). It concerns applying AI/ML practices, including Reinforcement Learning (RL) and Deep Neural Networks to numerically solve complex models in control theory, which are challenging using traditional methods. In this fellowship, we are going to develop a novel hybrid controller of MPC-RL with uncertainty quantification (UQ) for microreactor applications that combine the best of both worlds: RL develops a model that learns from previous states, while MPC ensures RL is fully respecting the physical model constraints by resolving neural network extrapolation issues.
- AI Mentor: Majdi Radaideh, Alex Gorodetsky, Aerospace Engineering, College of Engineering
- Science Mentor: Brendan Kochunas, Nuclear Engineering and Radiological Science, College of Engineering