  The magnetic field plays an essential role in the initiation and evolution of
different solar phenomena in the corona. The structure and evolution of the 3D
coronal magnetic field are still not very well known. A way to get the 3D
structure of the coronal magnetic field is by performing magnetic field
extrapolations from the photosphere to the corona. In previous work, it was
shown that by prescribing the 3D reconstructed loops' geometry, the magnetic
field extrapolation finds a solution with a better agreement between the
modeled field and the reconstructed loops. Also, it improves the quality of the
field extrapolation. Stereoscopy represents the classical method for performing
3D coronal loop reconstruction. It uses at least two view directions. When only
one vantage point of the coronal loops is available, other 3D reconstruction
methods must be applied. Within this work, we present a method for the 3D loop
reconstruction based on machine learning. Our purpose for developing this
method is to use as many observed coronal loops in space and time for the
modeling of the coronal magnetic field. Our results show that we can build
machine learning models that can retrieve 3D loops based only on their
projection information. In the end, the neural network model will be able to
use only 2D information of the coronal loops, identified, traced and extracted
from the EUV images, for the calculation of their 3D geometry.
