  In this work, we present a parallel scheme for machine learning of partial
differential equations. The scheme is based on the decomposition of the
training data corresponding to spatial subdomains, where an individual neural
network is assigned to each data subset. Message Passing Interface (MPI) is
used for parallelization and data communication. We use convolutional neural
network layers (CNN) to account for spatial connectivity. We showcase the
learning of the linearized Euler equations to assess the accuracy of the
predictions and the efficiency of the proposed scheme. These equations are of
particular interest for aeroacoustic problems. A first investigation
demonstrated a very good agreement of the predicted results with the simulation
results. In addition, we observe an excellent reduction of the training time
compared to the sequential version, providing an almost perfect scalability up
to 64 CPU cores.
