NEUP CINR-23-29815: Bayesian Optimization for Automatic Reactor Design Optimization

The objective of this project is to develop analytical tools based on Gaussian process modeling and Bayesian Optimization that facilitate reactor design optimization by modeling the responses from the physics simulator. we will apply existing capabilities in AI field and adapt them to address the key characteristics of nuclear reactor design problem. Our project will automate the simulation-based design procedure, reduce the number of iterations, and finally minimize the design cycle time.

Additional Info

Field	Value
Awarded Institution	Arizona State University
PI	Andi Wang