We investigate the performance of fully convolutional networks to simulate
the motion and interaction of surface waves in open and closed complex
geometries. We focus on a U-Net architecture and analyse how well it
generalises to geometric configurations not seen during training. We
demonstrate that a modified U-Net architecture is capable of accurately
predicting the height distribution of waves on a liquid surface within curved
and multi-faceted open and closed geometries, when only simple box and
right-angled corner geometries were seen during training. We also consider a
separate and independent 3D CNN for performing time-interpolation on the
predictions produced by our U-Net. This allows generating simulations with a
smaller time-step size than the one the U-Net has been trained for.