NEUP CINR-24-31356: Improving the computational efficiency and usability of dynamic PRA with reinforcement learning

The overall objective of the proposed research is to improve the efficiency and usability of dynamic probabilistic risk assessment (PRA). Specifically, the first objective is to develop a new algorithm for dynamic PRA analysis that can significantly increase the computational efficiency. The second objective is to develop a question-answering system to streamline the process of risk-informed decision-making based on results obtained from the dynamic PRA analysis using the new algorithm.

Additional Info

Field	Value
Awarded Institution	University of Maryland, College Park
PI	Yunfei Zhao