Deep Learning for Control of Off-design Processes in Nuclear Plants

Student: Sukharkov Alexander

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2021

The World knows tragic nuclear accidents. Nowadays every process inside the reactor is controlled by a physical equation and the parameters on which the equation changes. There are a lot of parameters and we want to know which set is suitable and can be used and which one should not be used. To solve this task we decided to create our approach which is based on bifurcation theory, normalizing flows, and deep learning. This method should replace the existing one because of its computational speed and efficiency.

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