论文标题
使用卷积网络模拟表面波动力学
Simulating Surface Wave Dynamics with Convolutional Networks
论文作者
论文摘要
我们研究了完全卷积网络的性能，以模拟开放和封闭的复杂几何形状中表面波的运动和相互作用。我们专注于U-NET体系结构，并分析它对训练期间看不到的几何配置的概括程度。我们证明，修改后的U-NET结构能够准确预测弯曲和多面的开放和封闭几何形状内液体表面上波的高度分布，而在训练过程中只有简单的盒子和右角几何形状。我们还考虑了一个独立且独立的3D CNN，用于对我们的U-NET产生的预测进行时间插值。这允许生成比U-NET已训练的时间步长尺寸较小的模拟。
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.
