  We propose an Artificial Neural Network (ANN) design to solve the inverse
problem for a 1D Gross-Pitaevskii equation (GPE). More precise, the ANN takes
the squared modulus of the stationary GPE solution as an input and returns the
parameters of the potential function and the factor in front of the GPE
non-linear term. From the physical point of view the ANN predicts the
parameters of a trap potential and the interaction constant of 1D Bose-Einstein
Condensate (BEC) by its density distribution. Using the results of numerical
solution of GPE for more than $30 000$ sets of GPE parameters as train and
validation datasets we build the ANN as a fast and accurate inverse GPE solver.
