Calculating real-time surface deformation for large active surface radio antennas using a graph neural network

This paper presents an innovative surrogate modeling method using a graph neural network to compensate for gravitational and thermal deformation in large radio telescopes. Traditionally, rapid compensation is feasible for gravitational deformation but not for temperature-induced deformation. The introduction of this method facilitates real-time calculation of deformation caused both by gravity and temperature. Constructing the surrogate model involves two key steps. First, the gravitational and thermal loads are encoded, which facilitates more efficient learning for the neural network. This is followed by employing a graph neural network as an end-to-end model. This model effectively maps external loads to deformation while preserving the spatial correlations between nodes. Simulation results affirm that the proposed method can successfully estimate the surface deformation of the main reflector in real-time and can deliver results that are practically indistinguishable from those obtained using finite element analysis. We also compare the proposed surrogate model method with the out-of-focus holography method and yield similar results.