On the feasibility of machine-learning model for neutronic analyses of pressurized water reactor fuel assemblies

Neutronic analysis of nuclear fuel assemblies is crucial for optimizing reactor core design. The conventional techniques involve computationally expensive simulations. This work explores the feasibility of developing efficient machine-learning models for neutronic analysis. This was done by leveragi