  The accurate construction of tokamak equilibria, which is critical for the
effective control and optimization of plasma configurations, depends on the
precise distribution of magnetic fields and magnetic fluxes. Equilibrium
fitting codes, such as EFIT relying on traditional equilibrium algorithms,
require solving the GS equation by iterations based on the least square method
constrained with measured magnetic signals. The iterative methods face numerous
challenges and complexities in the pursuit of equilibrium optimization.
Furthermore, these methodologies heavily depend on the expertise and practical
experience, demanding substantial resource allocation in personnel and time.
This paper reconstructs magnetic equilibria for the EAST tokamak based on
artificial neural networks through a supervised learning method. We use a fully
connected neural network to replace the GS equation and reconstruct the
poloidal magnetic flux distribution by training the model based on EAST
datasets. The training set, validation set, and testing set are partitioned
randomly from the dataset of poloidal magnetic flux distributions of the EAST
experiments in 2016 and 2017 years. The feasibility of the neural network model
is verified by comparing it to the offline EFIT results. It is found that the
neural network algorithm based on the supervised machine learning method can
accurately predict the location of different closed magnetic flux surfaces at a
high efficiency. The similarities of the predicted X-point position and last
closed magnetic surface are both 98%. The Pearson coherence of the predicted q
profiles is 92%. Compared with the target value, the model results show the
potential of the neural network model for practical use in plasma modeling and
real-time control of tokamak operations.
