论文标题
比较用于预测波传播的复发和卷积神经网络
Comparing recurrent and convolutional neural networks for predicting wave propagation
论文作者
论文摘要
动态系统可以通过部分微分方程进行建模，并且在科学和工程中，数值计算都在任何地方使用。在这项工作中，我们研究了复发和卷积深神经网络体系结构的性能，以预测表面波。该系统受圣人方程式的约束。我们对以前方法的长期预测进行了改进，同时将推理时间保持在数值模拟的一部分。我们还表明，在此任务中，卷积网络至少和经常性网络的性能。最后，我们通过在更长的时间范围和不同的物理设置中推断出每个网络的概括能力。
Dynamical systems can be modelled by partial differential equations and numerical computations are used everywhere in science and engineering. In this work, we investigate the performance of recurrent and convolutional deep neural network architectures to predict the surface waves. The system is governed by the Saint-Venant equations. We improve on the long-term prediction over previous methods while keeping the inference time at a fraction of numerical simulations. We also show that convolutional networks perform at least as well as recurrent networks in this task. Finally, we assess the generalisation capability of each network by extrapolating in longer time-frames and in different physical settings.
