This paper investigates the applicability of surrogate model optimization (SMO) using deep learning regression models to automatically embed knowledge about the objective function into the optimization process. This paper demonstrates two deep learning SMO methods for calculating simple neutronics parameters. Using these models, SMO returns results comparable with those from the early stages of direct iterative optimization. However, for this study, the cost of creating the training set outweighs the benefits of the surrogate models.
CITATION STYLE
Whyte, A., & Parks, G. (2020). Surrogate model optimization of a “micro core” PWR fuel assembly arrangement using deep learning models. In International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future, PHYSOR 2020 (Vol. 2020-March, pp. 2303–2310). EDP Sciences - Web of Conferences. https://doi.org/10.1051/epjconf/202124712003
Mendeley helps you to discover research relevant for your work.