AI-Powered New Modeling Method in Nuclear Reactor Analysis

A generative modeling framework is presented that learns from limited CFD data and performs physics validation to overcome the limitations of high-cost simulations. This method achieves a significant improvement in computational post-analysis by reducing the error rate in fuel assembly bowing inference from 5.1% to 1.86% in the BEAVRS benchmark test.