  In this work a series of artificial neural networks (ANNs) have been
developed with the capacity to estimate an obstacle's size and location
obstructing the flow in a pipe. The ANNs learn the size and location of the
obstacle by reading the profiles of the dynamic pressure $q$ or the
$x$-component of the velocity $v_x$ of the fluid at certain distance from the
obstacle. The data to train the ANN, was generated using numerical simulations
with a 2D Lattice Boltzmann code. We analyzed various cases varying both the
diameter and position of the obstacle on $y$-axis, obtaining good estimations
using the $R^2$ coefficient for the cases of study. Although the ANN showed
problems for the classification of the very small obstacles, the general
results show a very good capacity of prediction.
