Simulating Surface Wave Dynamics with Convolutional Networks
This work provides a method for more efficient and generalizable simulation of fluid dynamics for researchers and engineers working with wave phenomena in complex environments.
This paper explores the use of convolutional networks, specifically a modified U-Net architecture, to simulate surface wave dynamics in complex geometries. The U-Net successfully predicts wave height distribution in unseen curved and multi-faceted geometries, despite being trained only on simple box and right-angled corners. Additionally, a separate 3D CNN is used for time-interpolation, enabling simulations with smaller time-steps than the U-Net's training step.
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.