  Simulating complex dynamics like fluids with traditional simulators is
computationally challenging. Deep learning models have been proposed as an
efficient alternative, extending or replacing parts of traditional simulators.
We investigate graph neural networks (GNNs) for learning fluid dynamics and
find that their generalization capability is more limited than previous works
would suggest. We also challenge the current practice of adding random noise to
the network inputs in order to improve its generalization capability and
simulation stability. We find that inserting the real data distribution, e.g.
by unrolling multiple simulation steps, improves accuracy and that hiding all
domain-specific features from the learning model improves generalization. Our
results indicate that learning models, such as GNNs, fail to learn the exact
underlying dynamics unless the training set is devoid of any other
problem-specific correlations that could be used as shortcuts.
